34 Newborn Jaundice Screening Nomogram for Universal Screening in Saskatchewan
Bibliographic record
Abstract
Abstract Background Transcutaneous (TcB) meters support clinical decision making at point-of-care, reduces nurses’ time to screen, decreases the frequency of painful blood draws and minimizes health care costs. Best practice for TcB screening is unknown and international guidelines vary. Meter bias may result in over- or underestimation of TSB in a Canadian population. The Canadian Paediatric Society suggests screening between 24 and by 72 hours of age and reducing total serum bilirubin (TSB) action thresholds by the 95th confidence interval of the meter. To support point-of-care decision making, a tertiary center and community follow-up program uses universal screening and follow-up protocols. Objectives The aim of this analysis is to develop a locally validated TcB nomogram with action thresholds based on age and risk for clinical use at point-of-care. Design/Methods This prospective cross-sectional study includes newborns ≥35 weeks gestation <14 days old requiring TSB sampling in hospital or community and 13 JM-105 meters. Participants are included if TcB reading ranges from 1-340µmol/L (0.1-20.0 mg/dL) and TSB is collected within 1 hour of TcB measurement. TSB samples are analyzed using Roche™ Bilirubin Total Gen.3. To measure how close the TcB reading is to TSB, the difference is found by subtracting TSB from mean TcB. Lin’s Concordance statistics are calculated for each meter. Using a 2-dimentional 95th Confidence Interval ellipse, we select 13 JM-105 meters with similar accuracy and precision for use by the universal screening program. Using a quadratic model we fit a line based on the lower 95th predictive interval of the grouped meter data collected from 13 meters to Canadian Paediatric Society low, medium and high risk TSB thresholds for intensive phototherapy. Results The study population includes 498 newborns that received 620 visits and 705 meter readings with thirteen JM-105 with mean birth weight 3.38 kilograms (SD=0.51) with thirteen JM-105. Along with the clinical screening protocol, nomograms for newborns 35-37 weeks gestation (high and medium risk thresholds) and 38+ weeks gestation medium and low risk thresholds) are presented. Newborns with TcB that plots in a potential treatment range will receive a TSB to determine if intensive phototherapy is required. Conclusion To support point-of-care decision making, a tertiary center and community follow-up program uses universal screening and follow-up protocols. TcB nomograms with action thresholds based on age and risk support point-of-care decision making.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".