A transformation of oxygen saturation (the saturation virtual shunt) to improve clinical prediction model calibration and interpretation
Bibliographic record
Abstract
ABSTRACT Background The relationship between peripheral oxygen saturation (SpO 2 ) and the inspired oxygen concentration is non-linear. SpO 2 is frequently used as a dichotomized predictor, to manage this non-linearity. We propose the saturation virtual shunt (VS) as a transformation of SpO 2 to a continuous linear variable to improve interpretation of disease severity within clinical prediction models. Method We calculate the saturation VS based on an empirically derived approximation formula between physiological VS and SpO 2 . We evaluated the utility of the saturation VS in a clinical study predicting the need for facility admission in children in a low resource health-care setting. Results The transformation was saturation VS = 68.864*log 10 (103.711 − SpO 2 ) −52.110 . The ability to predict hospital admission based on a dichotomized SpO 2 produced an area under the receiver operating characteristic curve of 0.57, compared to 0.71 based on the untransformed SpO 2 and saturation VS . However, the untransformed SpO 2 demonstrated a lack of fit compared to the saturation VS (goodness-of-fit test p-value <0.0001 versus 0.098). The observed admission rates varied non-linearly with the untransformed SpO 2 but varied linearly with the saturation VS. Conclusion The saturation VS estimates a continuous linearly interpretable disease severity based on SpO 2 and improves clinical prediction.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".