Understanding the Allocation of Caesarean Outcome to Provider Type: A Chart Review
Bibliographic record
Abstract
Introduction: The concept, "most responsible provider" has a specific definition in the Canadian National Discharge Abstract Database (DAD).Variation exists in how care providers are defined in administrative data. Methods:We compared chart data with administrative data to understand how "most responsible provider" was identified in these two data sources.Results: We found a 3% discrepancy between data sources.Differences between data sources were attributable to transfers in care that occurred at birth.Discussion: "Most responsible provider" should consider the full trajectory of care when assigning outcomes in order to understand how to best support optimal health among lowrisk births. RésuméIntroduction : La définition du concept de « dispensateur principal » est précisée dans la Base de données sur les congés des patients (BDCP).Or, il existe certaines variations quant à la façon de définir les dispensateurs de services de santé dans les données administratives.Méthode : Nous avons comparé les données des dossiers aux données administratives pour comprendre comment le dispensateur est désigné dans ces deux sources de données.Résultats : Nous avons observé une divergence de 3 % entre les sources de données.Ces différences sont imputables aux transferts des soins qui ont lieux lors des naissances.Discussion : La notion de « dispensateur principal » doit tenir compte de l' ensemble de la trajectoire des soins au moment d' assigner les résultats, et ce, afin de comprendre la façon d' optimiser la santé dans les cas de naissances à faible risque.
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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 | 0.022 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".