Is it possible to estimate the minimal clinically important treatment effect needed to change practice in preterm birth prevention? Results of an obstetrician survey used to support the design of a trial
Bibliographic record
Abstract
BACKGROUND: Sample sizes for obstetrical trials are often based on the opinion of investigators about clinically important effect size. We surveyed Canadian obstetricians to investigate clinically important effect sizes required before introducing new treatments into practice to prevent preterm birth. METHODS: Questionnaires were mailed to practicing obstetricians, asking the magnitude of pregnancy prolongation required to introduce treatments into practice. The three prophylactic treatments were of increasing invasiveness: vaginal progesterone, intramuscular progesterone, and cervical cerclage. We also asked about the perceived most relevant outcome measures for obstetrical trials and current obstetrical practice in preterm birth prevention. RESULTS: 544/1293(42.1%) completed questionnaires were received. The majority of respondents required one or two weeks' increase in length of gestation before introducing vaginal (372,77.1%), and intramuscular progesterone(354,67.9%). At least three weeks increase was required before introducing prophylactic cervical cerclage(326,62.8%). Clinicians who already used a treatment required a smaller difference before introducing it into practice. Decreasing neonatal morbidity was cited as the most important outcome for obstetrical trials (349,72.2%). CONCLUSION: Obstetricians would require a larger increase in treatment effect before introducing more invasive treatments into practice. Although infant morbidity was perceived as a more important outcome, clinicians appeared willing to change practice on the basis of prolongation of pregnancy, a surrogate outcome. We found that there is not a single minimum clinically important treatment effect that will influence all practising clinicians: rather the effect size that will influence physicians is affected by the nature of the treatment, the reported outcome measure and the clinician's own current clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.131 | 0.494 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".