PubMed · 14804633
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J CUTHBERT. 1951. Current problems in tuberculosis.. https://doi.org/10.1177/146642405107100110
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PURPOSE: To determine the change in compliance with the Royal College of Ophthalmologists biometry guidelines since the last National Audit 2 years ago and in particular to quantify the adoption of modern methods of axial length measurement and customization of A constants. METHOD: A structured telephone questionnaire of individuals who perform biometry in all eye departments in the United Kingdom. RESULTS: A biometrist was interviewed in 94 of the 178 United Kingdom Ophthalmology departments. Compared with 2 years ago, nurses alone perform biometry more frequently (67 vs 51%) and junior doctors less frequently (9 vs 15%). More biometrists now attend external training courses (45 vs 37%). The Royal College of Ophthalmologists recommended intraocular lens calculation formulae (SRK-T, Hoffer Q, and Holladay) are used more commonly (30 and 15%) and audit of prediction error is being performed more frequently (78 vs 71%). The routine use of a partial coherence laser interferometry has increased from 35 to 61% in United Kingdom Ophthalmology departments. Currently, only one United Kingdom department is routinely using immersion ultrasound biometry. 'A' constants are customized in 47% of departments. CONCLUSION: Over the last 2 years, there has been improved implementation of the Royal College of Ophthalmologists guidelines on biometry. It is essential that the Royal College of Ophthalmologists guidelines are updated to include current best practice of routine use of partial coherence laser interferometry or immersion biometry and customization of A constants. A benchmark standard of 85-90% of patients achieving a final postoperative refraction within 1 dioptre of the predicted should be established.
The goal of this study is to assess whether a growth curve model approach will lead to a more precise detection of Turner sydnrome (TS) than conventional referral criteria for growth monitoring. The Jenss-Bayley growth curve model was used to describe the process of growth over time. A new screening rule is defined on the parameters of this growth curve model, parental height and gestational age. The rule is applied to longitudinal growth data of a group of children with TS (n=777) and a reference (n=487) group. The outcome measures are sensitivity, specificity and median referral age. Growth curve parameters for TS children were different from reference children and can therefore be used for screening. The Jenss-Bayley growth model, which uses all longitudinal measurements from birth to a maximum age of 5 years with at least one measurement after the age of 2, together with parental height and gestational age can achieve a sensitivity of 85.2 per cent with a specificity of 99.5 per cent and a median referral age of 4.2 (the last measurement between the age of 2 and 5 of each child is considered to be the moment of referral). Sensitivity increases by 2 percentage points when decreasing the specificity to 99 per cent. The Jenss-Bayley growth model from birth to a maximum age of 8 years with at least one measurement after the age of 2, together with parental height results in a sensitivity of 89.0 per cent with a specificity of 99.5 per cent and a median referral age of 6.1. For a specificity of 98 per cent, we obtain a sensitivity of 92.3 per cent. In comparison to conventional rules applied to the same data, sensitivity is about 11-30 percentage points higher at the same level of specificity for the Jenss-Bayley growth rule. We conclude that from the age of 4, growth curve models can improve the screening on TS to conventional screening rules.
The propensity score has been proposed, and for the most part accepted, as a tool to allow for the evaluation of medical interventions in the presence of baseline imbalances arising in the context of observational studies. The lack of an analogous tool to allow for the evaluation of medical interventions in the presence of potentially systematic baseline imbalances in randomized trials has required the use of ad hoc methods. This, in turn, leads to challenges to the conclusions. For example, much of the controversy surrounding recommendations for or against mammography for some age groups stems from the fact that all the randomized trials to study mammography had baseline imbalances, to some extent, in important prognostic covariates. While some of these trials used cluster randomization, baseline imbalances are prevalent also in individually randomized trials. We provide a systematic approach for evaluating medical interventions in the presence of potentially systematic baseline imbalances in individually randomized trials with allocation concealment. Specifically, we define the reverse propensity score as the probability, conditional on all previous allocations and the allocation procedure (restrictions on the randomization), that a given patient will receive a given treatment. We demonstrate how the reverse propensity score allows for both detection of and correction for selection bias, or systematic baseline imbalances.