Impact of skin tone on the performance of a transcutaneous jaundice meter
Bibliographic record
Abstract
AIM: To evaluate the performance of the Konica Minolta/Air-Shields JM-103 jaundice meter on the basis of infant skin tone during the early neonatal period. METHODS: Infants were prospectively categorized into light, medium and dark skin tone groups relative to two reference colours. Transcutaneous bilirubin readings were taken at predetermined intervals through the early neonatal period on a convenience sample of 938 healthy infants > or =37 weeks gestation. Serum bilirubin measurements were drawn routinely with metabolic studies and repeated in the presence of an elevated transcutaneous reading or clinically significant jaundice. RESULTS: Multivariate linear regression analysis showed a significant impact on serum and transcutaneous bilirubin agreement by skin tone. Highest precision and lowest bias were observed for medium skin toned infants. Greater disagreement between serum and transcutaneous measurements was noted at serum bilirubin concentrations >200 micromol/L. Insufficient numbers of dark skin toned infants were enrolled to evaluate fully the performance of the jaundice meter for this group. CONCLUSION: The JM-103 jaundice meter displayed good correlation with serum bilirubin concentrations in light and medium skin tone infants, although it showed a tendency to under-read in the lighter skin tone group and to over-read in the darker skin tone group. The device shows excellent performance characteristics for use as a screening device.
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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 | 0.007 | 0.052 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".