Pandemic planning: Developing a triage framework for Neonatal Intensive Care Unit
Bibliographic record
Abstract
Although the Covid-19 pandemic has not had a direct impact on neonates so far, it has raised concerns about resource distribution and showed that planning is required before the next crisis or pandemic. Resource allocation must consider unique Neonatal Intensive Care Unit (NICU) attributes, including physical space and equipment that may not be transferable to older populations, unique skills of NICU staff, inherent uncertainty in prognosis both antenatally and postnatally, possible biases against neonates, and the future pandemic disease's possible impact on neonates. We identified the need for a validated Neonatal Severity of Illness Prognostic Score to guide triage decisions. Based on this score, triage decisions are the responsibility of an informed triage team not involved in direct patient care. Support for the distress experienced by parents and staff is needed. This paper presents essential considerations in developing a practical framework for resources and triage in the NICU before, during and after a pandemic.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".