© 2007 Canadian Medical Association or its licensors
Bibliographic record
Abstract
T he practice of obstetrics has changed considerablyover the last several decades. In addition to the im-provements in obstetric outcomes because of prena-tal surveillance and treatments, the provision of obstetric care has also changed. The 24-hour availability of obstetri-cians that was typical in the past has evolved into a rotating call system, which, in addition to ensuring round-the-clock coverage, allows obstetricians to have a more predictable lifestyle and may reduce the number of medical errors.1–5 With these changes, however, there has been an increase in concern about quality of care. The question “will you be de-livering my baby? ” is frequently asked by pregnant women, which is understandable considering the comfort that comes with seeing one’s own doctor in the labour and de-livery setting. However, little is known about the differ-ences in obstetric outcomes experienced by women at-tended by their own prenatal care provider compared with an on-call physician. It is well established that the doctor–patient relationship is strongly associated with medicolegal action,6,7 and it has been suggested that the medicolegal pressure felt by obstetricians is associated with the rise in cesarean delivery rates.8–10 In this study, we sought to determine whether obstetric outcomes differ be-tween women attended by their own obstetrician and those attended by an on-call obstetrician. Materials and methods Study design We carried out a hospital-based cohort study using the
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.777 | 0.520 |
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; the direct Gemma label and the distilled Codex classifier 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".