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The Coronary Artery Bypass Graft Surgery Trajectory: Gender Differences Revisited

2009· article· en· W2076786964 on OpenAlexafffund
Jo‐Ann V. Sawatzky, Barbara J. Naimark

Bibliographic record

VenueEuropean Journal of Cardiovascular Nursing · 2009
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsUniversity of Manitoba
FundersUniversity of ManitobaResearch Manitoba
KeywordsMedicinePsychosocialQuality of life (healthcare)ArteryCoronary artery bypass surgeryPopulationSurgeryPhysical therapyNursingPsychiatry

Abstract

fetched live from OpenAlex

Over the past several decades there has been substantial research interest in gender differences within the coronary artery bypass graft (CABG) surgery trajectory. However, the debate persists regarding the reasons why women may have less favorable outcomes. As part of a larger study, we explored gender differences in the physiological and psychosocial dimensions of pre-operative status, and post-operative morbidity and quality of life outcomes in CABG surgery patients. A purposive sample of patients on the waiting list for CABG surgery (N=195; 157 males; 38 females) was followed for 6 months post-surgery. The results reflected consistent evidence of a male advantage across the CABG surgery trajectory. Though gender differences in age were non-significant, females had significantly more post-operative respiratory complications (p=0.005), a longer hospital stay (p=0.003), more symptoms at 2 weeks post-discharge, and a lower quality of life at 6 weeks and 6 months post-discharge. Our findings provide important insights for improving CABG surgery outcomes for both men and women. In particular, implementing creative strategies to improve physical functioning pre-operatively, may improve post-operative quality of life outcomes in this population.

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)0.005
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.974
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.041
GPT teacher head0.284
Teacher spread0.243 · 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 designOther design
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

Citations29
Published2009
Admission routes2
Has abstractyes

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