MétaCan
Menu
Back to cohort
Record W1990281380 · doi:10.1503/cjs.001012

High-concentration oxygen and surgical site infections in abdominal surgery: a meta-analysis

2013· review· en· W1990281380 on OpenAlexaffvenue
Sunil V. Patel, Shaun C. Coughlin, Richard Malthaner

Bibliographic record

VenueCanadian Journal of Surgery · 2013
Typereview
Languageen
FieldMedicine
TopicSurgical site infection prevention
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsMedicineSubgroup analysisConfidence intervalMeta-analysisSurgical site infectionRandomized controlled trialStatistical analysisSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: There has been recent interest in using high-concentration oxygen to prevent surgical site infections (SSIs). Previous meta-analyses in this area have produced conflicting results. With the publication of 2 new randomized controlled trials (RCTs) that were not included in previous meta-analyses, an updated review is warranted. Our objective was to perform a meta-analysis on RCTs comparing high- and low- concentration oxygen in adults undergoing open abdominal surgery. METHODS: We completed independent literature reviews using electronic databases, bibliographies and other sources of grey literature to identify relevant studies. We assessed the overall quality of evidence using grade guidelines. Statistical analysis was performed on pooled data from included studies. A priori subgroup analyses were planned to explain statistical and clinical heterogeneity. RESULTS: Overall, 6 studies involving a total of 2585 patients met the inclusion criteria. There was no evidence of a reduction in SSIs with high-concentration oxygen (risk ratio 0.77, 95% confidence interval 0.50-1.19, p = 0.24). We observed substantial heterogeneity among studies. CONCLUSION: There is moderate evidence that high-concentration oxygen does not reduce SSIs in adults undergoing open abdominal surgery.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.836
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0070.006
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.140
GPT teacher head0.333
Teacher spread0.192 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations17
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of SurgerySame topicSurgical site infection preventionFrench-language works237,207