MEASURING BIRTH OUTCOMES: VALIDATING THE PERINATAL OUTCOME INDEX
Bibliographic record
Abstract
We undertook a study to assess the reliability and validity of a new measure of low risk maternity care. A Perinatal Outcome Index (POI), which combines intrapartum process of care and clinical outcome items into a summary index score, was originally developed and evaluated in the Netherlands. It was designed to measure the extent to which a labour and birth are "optimal", that is, one with minimal intervention resulting in a healthy mother and a healthy baby. We modified the Dutch index to make it applicable to a Canadian setting. A panel of experts who were not connected with the study reviewed the modified version for applicability, feasibility of obtaining data easily, and content validity. Data were abstracted from the health records of 324 women in one hospital and two midwifery practices to obtain Perinatal Outcome Index scores and examine aspects of construct validity. We measured the inter-rater reliability of the research assistants who abstracted information. The panel achieved consensus on all items in the modified Perinatal Outcome Index to establish content (face) validity. Labour and birth data were readily obtained from health records with high inter-rater reliability (Kappa 0.78). In a linear regression model, birth at home, multiparity, and having a midwife or family physician as a care provider were significantly associated with higher scores (having a more optimal birth) and accounted for 37% of the score variance. The Perinatal Outcome Index has satisfied our expectations for content and construct validity. Research assistants found it easy to use and data items were readily available from women's health records. Inter-rater reliability was acceptable. We believe the modified index will be useful for comparative studies among women at low or average risk, and for quality assurance programs.
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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 | 0.050 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".