S M Nazmuz Sakib Jaundice Closed-Loop Safety Number: A Dimensionless Index for Post-Discharge Risk in Neonatal Hyperbilirubinemia
Bibliographic record
Abstract
Neonatal hyperbilirubinemia remains one of the most common reasons for early post-discharge readmission, despite the widespread adoption of hour-specific serum bilirubin nomograms and modern guidelines by the American Academy of Pediatrics (AAP) and the Canadian Paediatric Society (CPS) [1, 2, 4]. Contemporary pathways emphasise the difference between the measured bilirubin concentration and the phototherapy threshold (∆TSB) and the need for timely follow-up after discharge [1, 6]. However, current practice does not express this follow-up logic as a single, dimensionless closed-loop safety parameter. In this methodological paper, S M Nazmuz Sakib introduces the S M Nazmuz Sakib Jaundice Closed-Loop Safety Number (hereafter, the Sakib Number), a dimensionless index that combines the predischarge bilirubin margin, a worst-case bilirubin rise rate, and the planned interval to the next bilirubin assessment. The associated Sakib Safety Principle states that safe follow-up programmes should satisfy S JN ≥ 1, meaning that even a worst-case rise should not cross the phototherapy threshold before the next planned assessment. We formally define the Sakib Number, relate it to existing AAP/CPS risk assessment strategies [1, 2, 6], and illustrate its behaviour using simple numerical examples constructed from published TcB-TSB correlation and readmission datasets [11, 3]. Additional examples show how the Sakib Number interacts with measurement technology (serum vs. transcuta-neous bilirubin) using data summaries from recent correlation studies [8, 10, 9, 11]. The 1 S M Nazmuz Sakib Jaundice Closed-Loop Safety Number Preprint Manuscript manuscript is intended as a conceptual and computational framework; definitive clinical validation requires analysis of real-world multi-centre individual-level datasets.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".