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Record W7117449573 · doi:10.1155/ijpe/4574683

Blood Gas Interpretation Under Hypothermic Conditions: A Comparative Study of Alpha‐Stat and pH‐Stat in Neonatal Hypoxic–Ischemic Encephalopathy

2025· article· en· W7117449573 on OpenAlexafffund
Vardhil Gandhi, Arijit Lodha, Khorshid Mohammad, A Lodha, J. N. Scott, Yacov Rabi

Bibliographic record

VenueInternational Journal of Pediatrics · 2025
Typearticle
Languageen
FieldMedicine
TopicNeonatal and fetal brain pathology
Canadian institutionsAlberta Children's HospitalUniversity of CalgaryUniversity of AlbertaCalgary Laboratory ServicesMcMaster University
FundersCalgary Laboratory Services
KeywordsBlood gas analysisEncephalopathyInterpretation (philosophy)Neonatal encephalopathyHypothermia

Abstract

fetched live from OpenAlex

Introduction Acid‐base management in neonates with hypoxic–ischemic encephalopathy (HIE) undergoing therapeutic hypothermia (TH) can use either the “alpha‐stat” or the “pH‐stat” method, which adjusts values based on temperature. Objective This study aimed to compare the efficacy of two blood gas analysis techniques in predicting the severity of brain injury in neonates with HIE. Method A retrospective study was conducted on neonates over 35 weeks’ gestation who underwent TH between 2010 and 2015. Diagnostic, univariate, and multivariate analyses were performed to compare outcomes between the alpha‐sat and pH‐stat groups. Results Adjusting for sex and age, the odds ratios for being classified as hypocapnic were 4.40 (95% CI: 1.19–16.27) using the alpha‐stat method and 2.94 (95% CI 1.15–26.48) using the pH‐stat method. The classification of patients as hypocapnic, normocapnic, or hypercapnic differed significantly between the two methods ( p < 0.0001), with 23% of patients reclassified from the alpha‐stat to the pH‐stat method. Conclusion Both blood gas analysis methods were similarly effective in predicting brain injury extent. However, the alpha‐stat method significantly overestimated the lowest pCO 2 values during therapeutic hypothermia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.314
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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