Effect of opaque wraps for pulse oximeter sensors: randomised cross-over trial
Bibliographic record
Abstract
BACKGROUND: Evidence is lacking as to whether ambient light or phototherapy light could interfere with pulse oximeter performance. METHODS: In this randomised cross-over trial, we recruited neonates of gestation >24 weeks. Consented infants were randomly assigned to either pulse oximeter sensor with opaque wrap or without opaque wrap. Nellcor and Masimo sensors were applied simultaneously to different feet for 10 min of recording. Infants were crossed over to the other intervention for a further 10 min, totalling 20 min recording per infant. Primary outcome was faster acquisition of data with shielding of pulse oximeter sensor as compared with not shielding. RESULTS: A total of 96 babies were recruited. There was no difference in primary outcome of time taken to display valid data between the two groups (opaque wrap: 12.73±3.1 s vs no opaque wrap: 13.16±3.3 s, p=0.27). There was no difference in any of the secondary outcomes (percentage of valid data points, percentage of time saturation below target, and so on) between the two groups in both pulse oximeters. Masimo sensor readings displayed a higher mean oxygen saturation (mean difference of 2.85, p=0.001) and lower percentage of time saturation below 94% (mean difference of -27.8, p=0.001) than Nellcor in both groups. There was no difference in any of the outcomes in babies receiving phototherapy (n=21). CONCLUSION: In this study, shielding the pulse oximeter sensor from ambient light or phototherapy light did not yield faster data acquisition or better data quality. TRIAL REGISTRATION NUMBER: ISRCTN10302534.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".