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F Davidoff

Publications and source records attributed to F Davidoff.

At least 37 records · Page 2Linked to original sources
PubMed1998

Changes to manuscripts during the editorial process: characterizing the evolution of a clinical paper.

CONTEXT: Biomedical manuscripts undergo substantive change as a result of the peer review and editorial revision processes. OBJECTIVE: To characterize quantitatively problems in manuscripts identified during peer review and changes made to address these problems. DESIGN AND SETTING: Descriptive analysis of manuscripts submitted to and articles published by the Annals of Internal Medicine. A taxonomy of problems that occur in reporting clinical research was developed from analysis of changes made to 7 manuscripts between submission and publication (published October 15, 1996, and November 1, 1996). The taxonomy was used to characterize changes to 12 additional manuscripts (published January 15, 1997, to April 1, 1997). MAIN OUTCOME MEASURE: Types of problems necessitating changes to manuscripts during peer review and revision. RESULTS: Changes occurred because of 5 types of problems: too much information, too little information, inaccurate information, misplaced information, and structural problems. Changes most often occurred because information was missing or extraneous. The distribution of changes seemed to be influenced by the type of information involved (such as background or conclusions). CONCLUSION: The proposed framework may be useful for characterizing quantitatively the effects of peer review and for comparing those effects across editors, journals, and specialties.

Peer Review↗
PubMed1997

Databases in the next millennium.

Predicting the likelihood that large databases will become an important instrument for medical quality improvement is at least as difficult as most prognostication. This attempt at prediction starts by trying to ask the right questions: Can data serve as the agent for meaningful improvement? Is meaningful improvement possible without data? What elements are necessary and sufficient for improving the quality of medical care? It then looks for answers, starting with an unconventional excursion into the history of database use. This is followed by a recognition that the use of large databases for medical quality improvement is a true innovation, the future of which will probably be determined as much by the social and emotional forces that govern the diffusion of all innovations as by the technical strength of databases themselves. Finally, it examines some of the limitations and pitfalls that are likely to be associated with the increasing use of large databases in medicine.

Databases, Factual↗
PubMed1997

Time.

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Anti-Inflammatory Agents, Non-Steroidal↗
PubMed1996

Important elements of outpatient care: a comparison of patients' and physicians' opinions.

OBJECTIVE: To compare patients' and physicians' opinions on the importance of discrete elements of health care as determinants of the quality of outpatient care. DESIGN: Analysis of results of a mailed survey. SETTING: Community-based internal medicine practices. PARTICIPANTS: 74 general internists and 814 patients randomly selected from the practices of these internists. MEASURES: 125 elements of care that covered nine domains were identified: physician clinical skill, physician interpersonal skill, support staff, office environment, provision of information, patient involvement, nonfinancial access, finances, and coordination of care. Participants rated each element on its importance to high-quality care on a 4-point scale: 1 = not important; 2 = of medium importance; 3 = of high importance; and 4 = essential. Patients' and physicians' ratings were compared for individual elements of care and for elements aggregated into domains. RESULTS: Survey response rates were 93% for physicians and 60% for patients. In an element-by-element comparison of ratings, ratings by the two groups differed substantially for 58% of the attributes. The most striking difference was seen in the domain of provision of information (median ratings, 3.56 for patients and 2.85 for physicians; P < 0.001). Ratings by the two groups also differed in the domains of clinical skill (3.75 for patients and 3.35 for physicians; P < 0.001), nonfinancial access (3.00 for patients and 2.87 for physicians; P < 0.001), and finances (3.00 for patients and 2.80 for physicians; P = 0.006). When relative rankings of the domains were compared, both groups agreed that clinical skill is most important; however, patients ranked provision of information second in importance whereas physicians ranked it sixth. CONCLUSIONS: Patients and physicians agreed that the most crucial element of outpatient care is clinical skill, but they disagreed about the relative importance of other aspects of care, particularly effective communication of health-related information. These differences in perception may influence the quality of interactions between physicians and patients.

Adolescent↗
PubMed1996

Everyone sang.

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Humans↗
PubMed1996

Predicting clinical states in individual patients.

A probability model expresses the relation between the presence of clinical findings (input or independent variables) and the probability that a clinical state will occur (the dependent variable); for example, it expresses the probability that a disease is present or will develop or the probability that an outcome state will be reached. Probability models are developed by using selected study groups. Although these models are most often used to make predictions for groups of patients, they can also predict clinical states for individual patients. The following seven criteria provide a basis for the critical appraisal of probability models. In particular, physicians can use these criteria to decide when a specific probability model should be used to make a prediction in an individual patient. Five of the criteria are concerned with the applicability of a model to a particular patient: 1) the comparability of the patient and the study group used to develop the model; 2) the congruence between the clinical state of interest to patient and physician and the model's outcome; 3) the availability of all input variables where and when the prediction is to be made; 4) the usefulness of a quantitative estimate of the predicted clinical state; and 5) the degree of uncertainty in the probability estimate. The other two criteria are concerned with how well the probability model "works": 6) the fit of probabilities calculated from the model to the outcomes actually observed and 7) the model's ability to discriminate between outcome states relative to chance and to other, more traditional, prediction methods. We illustrate the use of these criteria by applying them, in the form of questions, to a convenient, tabular version of a model that estimates a patient's chances of surviving for 10 years after having definitive surgical therapy for primary cutaneous melanoma.

Adult↗