Inter-rater agreement on the protocol for care and risk classification in obstetrics
Bibliographic record
Abstract
Abstract Objective To determine the degree of agreement, sensitivity and specificity of the priority of care determined by inter-rater nurses, based on the use of the protocol for care and risk classification in obstetrics, in an obstetric emergency unit. Method Cross-sectional study with a methodological approach, carried out in a maternity school in Belo Horizonte-MG-Brazil, from September to November 2020. It was carried out in two stages: 1) Documental with an evaluation of the records of nurse classifiers in the medical records of pregnant women, parturients or puerperal women; 2) Interviews with trained and not trained nurses in risk classification. Sensitivity and specificity were analyzed and the Kappa coefficient (k) was used to assess agreement. Results The degree of inter-rater agreement (trained and not trained nurses) was found to be moderate to strong (k= 0.47 and 0.77). There was a tendency to underestimate the red (sensitivity of 85%; specificity of 99%) and yellow priorities (sensitivity of 54%; specificity of 85%), as well as overestimate the green (sensitivity of 62%; specificity of 84%) and blue priorities (sensitivity of 89%, specificity of 98%), although there were no significant differences. Despite satisfactory agreement and specificity, sensitivity was low, due to the rates of underestimation and overestimation in risk classification. Conclusion The protocol is reliable for determining priority of care in obstetrics, but its sensitivity was low when applied to determining priority of care by trained and not trained nurses.
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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 | 0.297 | 0.333 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".