High inter‐rater reliability between physicians and nurses utilising modified Downes' scores in preterm respiratory distress
Bibliographic record
Abstract
AIM: To assess the inter-rater reliability of modified Downes' scores assigned by physicians and nurses in the Ethiopian Neonatal Network and to calculate the concordance of score-based treatment for preterm infants with respiratory distress. METHODS: We included preterm infants admitted from June 2020 to July 2021 to four tertiary neonatal intensive care units (NICUs) of the Ethiopian Neonatal Network that presented with respiratory distress. We calculated the kappa statistic to determine the nurse and physician correlation for each component of the modified Downes' score and total score on admission and evaluated the concordance of scores above and below the treatment threshold of 4. RESULTS: Of the 1151 eligible infants admitted, 817 infants (71%) had scores reported concurrently and independently by nurse and physician. The kappa statistic for modified Downes' score components ranged from 0.88 to 0.92 and was 0.89 for the total score. There was 98% concordance for score-based treatment. CONCLUSION: Incorporation of the modified Downes' score on admission for preterm infants with respiratory distress was feasible in tertiary NICUs in Ethiopia. The kappa statistics showed near-perfect agreement between nurse and physician assessments, translating to a very high degree of concordance in score-based treatment recommendations. These results highlight an opportunity for task-shifting assessments and empowering nurses.
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How this classification was reachedexpand
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".