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David A Grimes

Publications and source records attributed to David A Grimes.

At least 19 recordsLinked to original sources
PubMed2003

Effect of lactation on maternal body weight: a systematic review.

UNLABELLED: The impact of lactation on maternal weight after delivery remains unclear. To address this question, we performed a systematic review of the literature. We searched PubMed, POPLINE, EMBASE, and LILACS computerized databases for relevant articles and classified the evidence using the U.S. Preventive Services Task Force rating system. We found 42 studies, of which 28 contained relevant information: 15 prospective cohort studies, 1 retrospective cohort study, 1 cross-sectional study, and 11 case-series reports. The methods used in these studies varied widely, thus precluding aggregation of results. Level II-2 and III evidence supports a class "C" recommendation: insufficient evidence exists to support an effect of lactation on maternal weight after delivery. Better longitudinal studies with a clear definition of breast-feeding; clear, specific outcome measures; study periods extending 1 or 2 years after delivery; and better control of potentially confounding factors will be needed to resolve this question. TARGET AUDIENCE: Obstetricians & Gynecologists, Family Physicians. LEARNING OBJECTIVES: After completion of this article, the reader will be able to compile the literature on the effect of lactation on maternal weight, to compare the various studies, and to summarize the data on the effect of lactation on maternal weight gain.

Body Weight↗
PubMed2002

Uncertainty.

Explore the source record for details and available documents.

Anecdotes as Topic↗
PubMed2002

Emergency contraception.

Emergency contraception is used to prevent pregnancy after a coital act not adequately protected by a regular method of contraception. In contrast to early medical abortion, emergency contraception prevents a pregnancy from starting and does not disrupt an established pregnancy. The most commonly used approaches consist of two oral doses of contraceptive steroids. The levonorgestrel-only regimen (levonorgestrel, 0.75 mg, repeated in 12 hours) appears to be more effective and better tolerated than the Yuzpe regimen (ethinyl estradiol, 100 microg, and levonorgestrel, 0.5 mg, repeated in 12 hours). In the largest randomized, controlled trial to date, levonorgestrel prevented about 85% of pregnancies that would have occurred without its use. Hormonal emergency contraception has no known medical contraindications, although it is not indicated for suspected or confirmed pregnancy. However, if hormonal emergency contraception is inadvertently taken in early pregnancy, neither the woman nor the fetus will be harmed. Nausea and vomiting associated with the Yuzpe regimen can be reduced by prophylactic use of meclizine. A strong medical and legal case exists for making hormonal emergency contraception available over the counter, as has happened in countries other than the United States. Easier access to and wider use of emergency contraception could dramatically lower the high rates of unintended pregnancy and induced abortion in the United States.

Age Factors↗
PubMed2002

Unequal group sizes in randomised trials: guarding against guessing.

We cringe at the pervasive notion that a randomised trial needs to yield equal sample sizes in the comparison groups. Unfortunately, that conceptual misunderstanding can lead to bias by investigators who force equality, especially if by non-scientific means. In simple, unrestricted, randomised trials (analogous to repeated coin-tossing), the sizes of groups should indicate random variation. In other words, some discrepancy between the numbers in the comparison groups would be expected. The appeal of equal group sizes in a simple randomised controlled trial is cosmetic, not scientific. Moreover, other randomisation schemes, termed restricted randomisation, force equality by departing from simple randomisation. Forcing equal group sizes, however, potentially harms the unpredictability of treatment assignments, especially when using permuted-block randomisation in non-double-blinded trials. Diminished unpredictability can allow bias to creep into a trial. Overall, investigators underuse simple randomisation and overuse fixed-block randomisation. For non-double-blinded trials larger than 200 participants, investigators should use simple randomisation more often and accept moderate disparities in group sizes. Such unpredictability reflects the essence of randomness. We endorse the generation of mildly unequal group sizes and encourage an appreciation of such inequalities. For non-double-blinded randomised controlled trials with a sample size of less than 200 overall or within any principal stratum or subgroup, urn randomisation enhances unpredictability compared with blocking. A simpler alternative, our mixed randomisation approach, attains unpredictability within the context of the currently understood simple randomisation and permuted-block methods. Simple randomisation contributes the unpredictability whereas permuted-block randomisation contributes the balance, but avoids the perfect balance that can result in selection bias.

Bias↗
PubMed2002

Uses and abuses of screening tests.

Screening tests are ubiquitous in contemporary practice, yet the principles of screening are widely misunderstood. Screening is the testing of apparently well people to find those at increased risk of having a disease or disorder. Although an earlier diagnosis generally has intuitive appeal, earlier might not always be better, or worth the cost. Four terms describe the validity of a screening test: sensitivity, specificity, and predictive value of positive and negative results. For tests with continuous variables--eg, blood glucose--sensitivity and specificity are inversely related; where the cutoff for abnormal is placed should indicate the clinical effect of wrong results. The prevalence of disease in a population affects screening test performance: in low-prevalence settings, even very good tests have poor predictive value positives. Hence, knowledge of the approximate prevalence of disease is a prerequisite to interpreting screening test results. Tests are often done in sequence, as is true for syphilis and HIV-1 infection. Lead-time and length biases distort the apparent value of screening programmes; randomised controlled trials are the only way to avoid these biases. Screening can improve health; strong indirect evidence links cervical cytology programmes to declines in cervical cancer mortality. However, inappropriate application or interpretation of screening tests can rob people of their perceived health, initiate harmful diagnostic testing, and squander health-care resources.

Ethics, Medical↗
PubMed2002

Sample size slippages in randomised trials: exclusions and the lost and wayward.

Proper randomisation means little if investigators cannot include all randomised participants in the primary analysis. Participants might ignore follow-up, leave town, or take aspartame when instructed to take aspirin. Exclusions before randomisation do not bias the treatment comparison, but they can hurt generalisability. Eligibility criteria for a trial should be clear, specific, and applied before randomisation. Readers should assess whether any of the criteria make the trial sample atypical or unrepresentative of the people in which they are interested. In principle, assessment of exclusions after randomisation is simple: none are allowed. For the primary analysis, all participants enrolled should be included and analysed as part of the original group assigned (an intent-to-treat analysis). In reality, however, losses frequently occur. Investigators should, therefore, commit adequate resources to develop and implement procedures to maximise retention of participants. Moreover, researchers should provide clear, explicit information on the progress of all randomised participants through the trial by use of, for instance, a trial profile. Investigators can also do secondary analyses on, for instance, per-protocol or as-treated participants. Such analyses should be described as secondary and non-randomised comparisons. Mishandling of exclusions causes serious methodological difficulties. Unfortunately, some explanations for mishandling exclusions intuitively appeal to readers, disguising the seriousness of the issues. Creative mismanagement of exclusions can undermine trial validity.

Bias↗
PubMed2002

Blinding in randomised trials: hiding who got what.

Blinding embodies a rich history spanning over two centuries. Most researchers worldwide understand blinding terminology, but confusion lurks beyond a general comprehension. Terms such as single blind, double blind, and triple blind mean different things to different people. Moreover, many medical researchers confuse blinding with allocation concealment. Such confusion indicates misunderstandings of both. The term blinding refers to keeping trial participants, investigators (usually health-care providers), or assessors (those collecting outcome data) unaware of the assigned intervention, so that they will not be influenced by that knowledge. Blinding usually reduces differential assessment of outcomes (information bias), but can also improve compliance and retention of trial participants while reducing biased supplemental care or treatment (sometimes called co-intervention). Many investigators and readers naïvely consider a randomised trial as high quality simply because it is double blind, as if double-blinding is the sine qua non of a randomised controlled trial. Although double blinding (blinding investigators, participants, and outcome assessors) indicates a strong design, trials that are not double blinded should not automatically be deemed inferior. Rather than solely relying on terminology like double blinding, researchers should explicitly state who was blinded, and how. We recommend placing greater credence in results when investigators at least blind outcome assessments, except with objective outcomes, such as death, which leave little room for bias. If investigators properly report their blinding efforts, readers can judge them. Unfortunately, many articles do not contain proper reporting. If an article claims blinding without any accompanying clarification, readers should remain sceptical about its effect on bias reduction.

Double-Blind Method↗
PubMed2002

Allocation concealment in randomised trials: defending against deciphering.

Proper randomisation rests on adequate allocation concealment. An allocation concealment process keeps clinicians and participants unaware of upcoming assignments. Without it, even properly developed random allocation sequences can be subverted. Within this concealment process, the crucial unbiased nature of randomised controlled trials collides with their most vexing implementation problems. Proper allocation concealment frequently frustrates clinical inclinations, which annoys those who do the trials. Randomised controlled trials are anathema to clinicians. Many involved with trials will be tempted to decipher assignments, which subverts randomisation. For some implementing a trial, deciphering the allocation scheme might frequently become too great an intellectual challenge to resist. Whether their motives indicate innocent or pernicious intents, such tampering undermines the validity of a trial. Indeed, inadequate allocation concealment leads to exaggerated estimates of treatment effect, on average, but with scope for bias in either direction. Trial investigators will be crafty in any potential efforts to decipher the allocation sequence, so trial designers must be just as clever in their design efforts to prevent deciphering. Investigators must effectively immunise trials against selection and confounding biases with proper allocation concealment. Furthermore, investigators should report baseline comparisons on important prognostic variables. Hypothesis tests of baseline characteristics, however, are superfluous and could be harmful if they lead investigators to suppress reporting any baseline imbalances.

Adult↗
PubMed2002

Generation of allocation sequences in randomised trials: chance, not choice.

The randomised controlled trial sets the gold standard of clinical research. However, randomisation persists as perhaps the least-understood aspect of a trial. Moreover, anything short of proper randomisation courts selection and confounding biases. Researchers should spurn all systematic, non-random methods of allocation. Trial participants should be assigned to comparison groups based on a random process. Simple (unrestricted) randomisation, analogous to repeated fair coin-tossing, is the most basic of sequence generation approaches. Furthermore, no other approach, irrespective of its complexity and sophistication, surpasses simple randomisation for prevention of bias. Investigators should, therefore, use this method more often than they do, and readers should expect and accept disparities in group sizes. Several other complicated restricted randomisation procedures limit the likelihood of undesirable sample size imbalances in the intervention groups. The most frequently used restricted sequence generation procedure is blocked randomisation. If this method is used, investigators should randomly vary the block sizes and use larger block sizes, particularly in an unblinded trial. Other restricted procedures, such as urn randomisation, combine beneficial attributes of simple and restricted randomisation by preserving most of the unpredictability while achieving some balance. The effectiveness of stratified randomisation depends on use of a restricted randomisation approach to balance the allocation sequences for each stratum. Generation of a proper randomisation sequence takes little time and effort but affords big rewards in scientific accuracy and credibility. Investigators should devote appropriate resources to the generation of properly randomised trials and reporting their methods clearly.

Epidemiology↗
PubMed2002

Case-control studies: research in reverse.

Epidemiologists benefit greatly from having case-control study designs in their research armamentarium. Case-control studies can yield important scientific findings with relatively little time, money, and effort compared with other study designs. This seemingly quick road to research results entices many newly trained epidemiologists. Indeed, investigators implement case-control studies more frequently than any other analytical epidemiological study. Unfortunately, case-control designs also tend to be more susceptible to biases than other comparative studies. Although easier to do, they are also easier to do wrong. Five main notions guide investigators who do, or readers who assess, case-control studies. First, investigators must explicitly define the criteria for diagnosis of a case and any eligibility criteria used for selection. Second, controls should come from the same population as the cases, and their selection should be independent of the exposures of interest. Third, investigators should blind the data gatherers to the case or control status of participants or, if impossible, at least blind them to the main hypothesis of the study. Fourth, data gatherers need to be thoroughly trained to elicit exposure in a similar manner from cases and controls; they should use memory aids to facilitate and balance recall between cases and controls. Finally, investigators should address confounding in case-control studies, either in the design stage or with analytical techniques. Devotion of meticulous attention to these points enhances the validity of the results and bolsters the reader's confidence in the findings.

Case-Control Studies↗
PubMed2002

Cohort studies: marching towards outcomes.

A cohort study tracks two or more groups forward from exposure to outcome. This type of study can be done by going ahead in time from the present (prospective cohort study) or, alternatively, by going back in time to comprise the cohorts and following them up to the present (retrospective cohort study). A cohort study is the best way to identify incidence and natural history of a disease, and can be used to examine multiple outcomes after a single exposure. However, this type of study is less useful for examination of rare events or those that take a long time to develop. A cohort study should provide specific definitions of exposures and outcomes: determination of both should be as objective as possible. The control group (unexposed) should be similar in all important respects to the exposed, with the exception of not having the exposure. Observational studies, however, rarely achieve such a degree of similarity, so investigators need to measure and control for confounding factors. Reduction of loss to follow-up over time is a challenge, since differential losses to follow-up introduce bias. Variations on the cohort theme include the before-after study and nested case-control study (within a cohort study). Strengths of a cohort study include the ability to calculate incidence rates, relative risks, and 95% CIs. This format is the preferred way of presenting study results, rather that with p values.

Bias↗
PubMed2002

Bias and causal associations in observational research.

Readers of medical literature need to consider two types of validity, internal and external. Internal validity means that the study measured what it set out to; external validity is the ability to generalise from the study to the reader's patients. With respect to internal validity, selection bias, information bias, and confounding are present to some degree in all observational research. Selection bias stems from an absence of comparability between groups being studied. Information bias results from incorrect determination of exposure, outcome, or both. The effect of information bias depends on its type. If information is gathered differently for one group than for another, bias results. By contrast, non-differential misclassification tends to obscure real differences. Confounding is a mixing or blurring of effects: a researcher attempts to relate an exposure to an outcome but actually measures the effect of a third factor (the confounding variable). Confounding can be controlled in several ways: restriction, matching, stratification, and more sophisticated multivariate techniques. If a reader cannot explain away study results on the basis of selection, information, or confounding bias, then chance might be another explanation. Chance should be examined last, however, since these biases can account for highly significant, though bogus results. Differentiation between spurious, indirect, and causal associations can be difficult. Criteria such as temporal sequence, strength and consistency of an association, and evidence of a dose-response effect lend support to a causal link.

Bias↗
PubMed2002

Descriptive studies: what they can and cannot do.

Descriptive studies often represent the first scientific toe in the water in new areas of inquiry. A fundamental element of descriptive reporting is a clear, specific, and measurable definition of the disease or condition in question. Like newspapers, good descriptive reporting answers the five basic W questions: who, what, why, when, where. and a sixth: so what? Case reports, case-series reports, cross-sectional studies, and surveillance studies deal with individuals, whereas ecological correlational studies examine populations. The case report is the least-publishable unit in medical literature. Case-series reports aggregate individual cases in one publication. Clustering of unusual cases in a short period often heralds a new epidemic, as happened with AIDS. Cross-sectional (prevalence) studies describe the health of populations. Surveillance can be thought of as watchfulness over a community; feedback to those who need to know is an integral component of surveillance. Ecological correlational studies look for associations between exposures and outcomes in populations-eg, per capita cigarette sales and rates of coronary artery disease-rather than in individuals. Three important uses of descriptive studies include trend analysis, health-care planning, and hypothesis generation. A frequent error in reports of descriptive studies is overstepping the data: studies without a comparison group allow no inferences to be drawn about associations, causal or otherwise. Hypotheses about causation from descriptive studies are often tested in rigorous analytical studies.

Bias↗
PubMed2002

An overview of clinical research: the lay of the land.

Many clinicians report that they cannot read the medical literature critically. To address this difficulty, we provide a primer of clinical research for clinicians and researchers alike. Clinical research falls into two general categories: experimental and observational, based on whether the investigator assigns the exposures or not. Experimental trials can also be subdivided into two: randomised and non-randomised. Observational studies can be either analytical or descriptive. Analytical studies feature a comparison (control) group, whereas descriptive studies do not. Within analytical studies, cohort studies track people forward in time from exposure to outcome. By contrast, case-control studies work in reverse, tracing back from outcome to exposure. Cross-sectional studies are like a snapshot, which measures both exposure and outcome at one time point. Descriptive studies, such as case-series reports, do not have a comparison group. Thus, in this type of study, investigators cannot examine associations, a fact often forgotten or ignored. Measures of association, such as relative risk or odds ratio, are the preferred way of expressing results of dichotomous outcomes-eg, sick versus healthy. Confidence intervals around these measures indicate the precision of these results. Measures of association with confidence intervals reveal the strength, direction, and a plausible range of an effect as well as the likelihood of chance occurrence. By contrast, p values address only chance. Testing null hypotheses at a p value of 0.05 has no basis in medicine and should be discouraged.

Algorithms↗
PubMed2002

Large French-Canadian family with Lewy body parkinsonism: exclusion of known loci.

The identification of rare, large families with Parkinson's disease (PD) has provided important clues that have contributed to our understanding of this complex disorder. We have identified a large French-Canadian kindred that spans five generations consisting of more than 90 individuals. A total of 65 individuals now have been examined, had venous blood drawn, and DNA extracted. Two-point and multipoint linkage analysis was performed to assess linkage to known PD genes or loci. Within the third and fourth generations of this family there are 10 living, plus 3 deceased members with well-documented levodopa responsive parkinsonism. Autopsy results on 1 member demonstrated the loss of pigmented neurons in the substantia nigra and the presence of alpha-synuclein positive Lewy bodies. Four of the PD patients have prominent postural and kinetic tremors that preceded their parkinsonism by up to 10 years. Two other individuals within the family have prominent isolated postural and kinetic tremors without parkinsonism. The alpha-synuclein(4q21.3-23), Parkin(6q25.2-27), PARK3 (2p13), PARK4, and ubiquitin carboxy terminal hydrolase-L1 (4p14-16.3) and PARK6 and PARK7 (1p35-36) loci were excluded in this kindred using closely linked markers. The clinical and pathological features of this family are consistent with the diagnosis of PD. This family further demonstrates the known genetic heterogeneity in PD and is large enough that a genome-wide screen has been undertaken in an effort to identify a novel PD gene.

Adult↗