Prevention of postpartum haemorrhage: a distributional approach for analysis
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: There is empirical evidence that measured postpartum blood loss has a lognormal distribution. This feature can be used to analyze events of the type 'blood loss greater than a certain cutoff point' using a lognormal approach, which takes into account all the quantitative observations, as opposed to dichotomizing the variable blood loss volume into two categories. This lognormal approach uses all the information contained in the data and is expected to provide more efficient estimates of proportions and relative risk when comparing treatments to prevent postpartum haemorrhage. As a consequence, sample size can be reduced in clinical trials, while keeping the statistical precision requirements. METHODS: The authors illustrate how a lognormal approach can be used in this situation, using data from a clinical trial and the event 'blood loss greater than 1000 mL'. RESULTS: Estimates of the proportions of this event for each treatment, and relative risks obtained with this method are presented and compared with the standard estimates obtained by dichotomizing measured blood loss volume. An example of how the blood loss distributions of two treatments can be compared is also presented. Different scenarios of the sample size needed to compare two treatments or interventions are presented to illustrate how with the lognormal approach the size of a clinical trial can be reduced. CONCLUSIONS: A distributional approach for postpartum blood loss using the lognormal distribution fitted to the data results in more precise estimates of risks of events and relative risks, compared to the use of binomial proportions of events. It also results in reduced required sample size for clinical trials. TRIAL REGISTRATION: This paper reports a secondary analysis for a trial that was registered at clinicaltrials.gov ( NCT00781066 ).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it