Impact of a Transcutaneous Bilirubinometry Program on Resource Utilization and Severe Hyperbilirubinemia
Bibliographic record
Abstract
OBJECTIVES: Our goal was to assess the impact of programmatic and coordinated use of transcutaneous bilirubinometry (TcB) on the incidence of severe neonatal hyperbilirubinemia and measures of laboratory, hospital, and nursing resource utilization. METHODS: We compared the neonatal hyperbilirubinemia-related outcomes of 14 796 prospectively enrolled healthy infants ≥35 weeks gestation offered routine TcB measurements in both hospital and community settings by using locally validated nomograms relative to a historical cohort of 14 112 infants assessed by visual inspection alone. RESULTS: There was a 54.9% reduction (odds ratio [OR]: 2.219 [95% confidence interval (CI): 1.543-3.193]; P < .0001) in the incidence of severe total serum bilirubin values (≥342 µmol/L; ≥20 mg/dL) after implementation of routine TcB measurements. TcB implementation was associated with reductions in the overall incidence of total serum bilirubin draws (134.4 vs 103.6 draws per 1000 live births, OR: 1.332 [95% CI: 1.226-1.446]; P < .0001) and overall phototherapy rate (5.27% vs 4.30%, OR: 1.241 [95% CI: 1.122-1.374]; P < .0001), a reduced age at readmission for phototherapy (104.3 ± 52.1 vs 88.9 ± 70.5 hours, P < .005), and duration of phototherapy readmission (24.8 ± 13.6 vs 23.2 ± 9.8 hours, P < .05). There were earlier (P < .01) and more frequent contacts with public health nurses (1.33 vs 1.66, P < .01) after introduction of the TcB program. CONCLUSIONS: Integration of routine hospital and community TcB screening within a comprehensive public health nurse newborn follow-up program is associated with significant improvements in resource utilization and patient safety.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".