Influence of skin colour on diagnostic accuracy of the jaundice meter JM 103 in newborns
Bibliographic record
Abstract
AIM: To assess the diagnostic accuracy of the JM 103 as a screening tool for neonatal jaundice and explore differential effects based on skin colour. METHODS: We prospectively compared the transcutaneous bilirubin (TcB) and serum bilirubin (TSB) measurements of newborns over a 3 month-period. Skin colour was assigned via reference colour swatches. Diagnostic measures of the TcB/TSB comparison were made and clinically relevant TcB cut-off values were determined for each skin colour group. RESULTS: 451 infants (51 light, 326 medium and 74 dark skin colour) were recruited. The association between TcB and TSB was high for all skin colours (rs>0.9). The Bland-Altman analysis showed an absolute mean difference between the two measures of 13.3±26.4 µmol/L with broad limits of agreement (-39.4-66.0 µmol/L), with TcB underestimating TSB in light and medium skin colours and overestimating in dark skin colour. Diagnostic measures were also consistently high across skin colours, with no clinically significant differences observed. CONCLUSIONS: The JM 103 is a useful screening tool to identify infants in need of serum bilirubin, regardless of skin colour. The effect of skin colour on the accuracy of this device at high levels of serum bilirubin could not be assessed fully due to small numbers in the light and dark groups.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".