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Record W2138987921 · doi:10.1177/1054773812459753

Weight and Patients’ Decision to Undergo Cardiac Surgery

2012· article· en· W2138987921 on OpenAlexafffund
Kathryn King‐Shier, Pamela LeBlanc, Charles Mather, Sarah Sandham, Cydnee Seneviratne, Andrew Maitland

Bibliographic record

VenueClinical Nursing Research · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsMedicineIntensive care medicine

Abstract

fetched live from OpenAlex

Obese patients are less likely to have cardiac surgery than normal weight patients. This could be due to physician or patient decision-making. We undertook a qualitative descriptive study to explore the influence of obesity on patients' decision-making to have cardiac surgery. Forty-seven people referred for coronary artery bypass graft (CABG) surgery were theoretically sampled. Twelve people had declined cardiac surgery. Participants underwent in-depth interviews aimed at exploring their decision-making process. Data were analyzed using conventional content analysis. Though patients' weight did not play a role in their decision, their relationship with their cardiologist/surgeon, the rapidity and orchestration of the diagnosis and treatment, appraisal of risks and benefits, previous experience with other illness or others who had cardiac surgery, and openness to other alternatives had an impact. It is possible that there is a lack of comfort or acknowledgment by all parties in discussing the influence of weight on CABG surgery risks.

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)0.004
metaresearch head score (Gemma)0.018
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 score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.137
GPT teacher head0.485
Teacher spread0.348 · 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

Citations3
Published2012
Admission routes2
Has abstractyes

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