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 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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.103 | 0.002 |
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; both teacher heads agree on what is shown here.
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".