A Method on Assessing Complication-Base Risk Factors for Neonatal Morbidity: Application for Pattani Hospital Delivery
Bibliographic record
Abstract
We investigated risk factors for neonatal morbidity based on a database of 19,268 singleton maternal deliveries atPattani Hospital during the period from 1 October 1996 to 30 September 2005 inclusive. This database includesdemographics of the mother and delivery outcomes including birth weight, one- and five-minute Apgar scores, and atmost one complication selected from a list of 62 by the deliverer. In our study the neonatal risk associated with acomplication was defined by averaging the results given by 11 obstetricians who independently scored eachcomplication on a scale from 0 to 9 (highest risk to baby). Using logistic regression to adjust for demographic andpregnancy-history factors, we found risk that Muslim women have higher neonatal morbidity risks, particularly thoseassociated with severe pregnancy-induced hypertension, eclampsia and thick mecomium stain.
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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 | 0.025 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".