Eucapnic <scp>pH</scp> coupled with arterial cord <scp>pH</scp> improves hypoxic–ischemic encephalopathy prediction
Bibliographic record
Abstract
OBJECTIVE: To consider the classical use of "pH < 7.0 and/or a base deficiency ≥12 mmol/L" as markers of the risk of neonatal hypoxic-ischemic encephalopathy (HIE), recalling various criticisms of the use of these markers in favor of that of neonatal eucapnic pH, which appears to be a better marker of this risk. METHODS: Fifty-five cases of acidemia with pH < 7.00 were collected from a cohort from the Nice University Hospital with eight cases of HIE. We compared the receiver operating characteristics curves established from the positive likelihood ratio (+LR) for each case of: umbilical cord artery pH (pHa), neonatal eucapnic pH (pH euc-n) in isolation (not matched to pHa), and matched pHa to its own pH euc-n. RESULTS: The areas under the curve (AUC) are identical for pHa and pH euc-n, but AUC for the matched pair pHa-pH euc-n appears superior but non-significant because of the small number in our cohort. However, using the bootstrap method, the partial AUC for a sensitivity greater than 75% indicates the significant superiority (P < 0.01) of the matched pair pHa-pH euc-n approach. CONCLUSION: The originality of this study lies in the use of two methodologic approaches: (1) standardized partial analysis of the AUCs of the pHa curve and that of pHa matched to its own pH euc-n, and (2) bootstrap statistical technique, that allowed us to conclude (P < 0.01) that the combined use of pH measured at the cord coupled with its eucapnic correction is better for diagnosing metabolic acidosis and best predicting the risk of HIE.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".