Providing clarity around ethical discussion: development of a neonatal intervention score
Bibliographic record
Abstract
AIM: To develop a Neonatal Intervention Score (NIS) to describe the clinical trajectory of a neonate throughout their neonatal intensive care unit (NICU) admission. METHODS: The NIS was developed by modifying the Neonatal Therapeutic Intervention Scoring System (NTISS) to reflect illness severity, dependency on life-sustaining interventions and overall life trajectory on a longitudinal basis, rather than illness burden. Validity for longitudinal use within the NICU was tested by calculating the score for 99 preterm babies born less than 28 weeks at predetermined time points throughout their admission to tertiary level care at two institutions. RESULTS: A total of 1333 NISs were analysed, ranging from 0 to 32.5 (mean 9.77, SD 5.4). Internal consistency (Cronbach alpha) reached 0.8. NIS moderately correlated to both SNAPPE-II and SNAP-II (Spearman's rho = 0.47, p =< 0.001) within the first 24 hours. CONCLUSION: The NIS is a useful and reliable descriptive tool of relative illness severity and degree of medical interventions throughout a baby's admission. Integrating a longitudinal description of medical dependency of a patient may assist both clinical and ethical decision-making and empirical research by providing an objective account of a baby's clinical trajectory. Establishment of validity within individual institutions is required.
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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 | 0.020 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".