Are pregnancy outcomes associated with risk factor reporting in routinely collected perinatal data?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIM: To assess reporting characteristics of commonly dichotomised pregnancy outcomes (e.g. preterm/term birth); and to investigate whether behaviours (e.g. smoking), medical conditions (e.g. diabetes) or interventions (e.g. induction) were reported differently by pregnancy outcomes. METHODS: Further analysis of a previous validation study was undertaken, in which 1680 perinatal records were compared with data extracted from medical records. Continuous and polytomous variables were dichotomised, and risk factor reporting was assessed within the dichotomised outcome groups. Agreement, kappa, sensitivity and positive predictive value calculations were undertaken. RESULTS: Gestational age, birthweight, Apgar scores, perineal trauma, regional analgesia and baby discharge status (live birth/stillbirth) were reported with high accuracy and reliability when dichotomised (kappa values 0.95-1.00, sensitivities 94.7-100.0%). Although not statistically significant, there were trends for hypertension, infant resuscitation and instrumental birth to be more accurately reported among births with adverse outcomes. In contrast, smoking ascertainment tended to be poorer among preterm births and when babies were <2500 g. CONCLUSION: Dichotomising variables collected as continuous or polytomous variables in birth data results in accurate and well ascertained data items. There is no evidence of systematic differential reporting of risk factors.
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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 | 0.012 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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