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Effect of intra‐operative end‐tidal carbon dioxide partial pressure on tissue oxygenation

2003· article· en· W1993877176 on OpenAlexfundno aff
Ozan Akça, Edwin B. Liem, Mohammad-Irfan Suleman, Anthony G. Doufas, Susan Galandiuk, D.I. Sessler

Bibliographic record

VenueAnaesthesia · 2003
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNational Institutes of HealthUniversity of Toronto
KeywordsHypercapniaMedicineAnesthesiaOxygenationOxygen tensionpCO2Partial pressureOxygen saturationSubcutaneous tissuePerfusionOxygenAcidosisSurgeryCardiologyChemistry

Abstract

fetched live from OpenAlex

Postsurgical infection risk is correlated with subcutaneous tissue oxygenation. Mild hypercapnia augments cutaneous perfusion. We tested the hypothesis that peripheral tissue oxygenation increases as a function of arterial PCO2 in surgical patients. Twenty patients were randomly assigned to intra-operative end tidal PCO2 of 3.99 (control) or 5.99 kPa (hypercapnia). All other anaesthetic management was per protocol. Tissue oxygen partial pressure, transcutaneous oxygen tension, cerebral oxygen saturation, and cardiac output were measured. Mean (SD) subcutaneous tissue oxygen tension was 8.39 (1.86) kPa in control and 11.84 (2.53) kPa hypercapnia patients (p = 0.014). Cerebral oxygen saturation was 55 (4)% for control vs. 68 (9)% for hypercapnia (p = 0.004). Neither cardiac index nor transcutaneous tissue oxygen tension differed significantly between the groups. Mild intra-operative hypercapnia increased subcutaneous and cerebral oxygenation. Increases in subcutaneous tissue oxygen partial pressure similar to those observed in patients assigned to hypercapnia are associated with substantial reductions in wound infection risk.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.277
Teacher spread0.268 · 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 source (direct Gemma or distilled Codex), 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

Citations63
Published2003
Admission routes1
Has abstractyes

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