MétaCan
Menu
Back to cohort
Record W2955396269

Impact of preoperative education on pain management outcomes after coronary artery bypass graft surgery: a pilot.

2000· article· en· W2955396269 on OpenAlexaffabout
Judy Watt‐Watson, Bonnie Stevens, Judy Costello, Joel Katz, Graham J. Reid

Bibliographic record

VenuePubMed · 2000
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineRandomized controlled trialAnalgesicPsychological interventionIntervention (counseling)Physical therapyMcGill Pain QuestionnairePain controlArteryAnesthesiaPain managementSurgeryNursing
DOInot available

Abstract

fetched live from OpenAlex

Patients have been found to receive inadequate analgesia despite moderate to severe pain after coronary artery bypass graft (CABG) surgery. The purpose of this pilot study was to evaluate a preadmission educational booklet for patients undergoing their first uncomplicated CABG. A randomized controlled trial (RCT) was undertaken at the largest cardiovascular centre in Canada. Repeated measures were used to compare data from 3 interviews: at baseline, day 3, and day 5. Patients were randomly assigned to one of 3 groups at the preadmission clinic 2 to 7 days before surgery: (1) generic hospital booklet and videotape (control), (2) control + pain booklet, or (3) control + pain booklet and interview; 45 subjects completed all 3 interviews. Measures were the McGill Pain Questionnaire-Short Form and the American Pain Society Patient Outcome Questionnaire. For all groups, analgesic administration was inadequate (19.89[13.37] mg morphine equivalents/24 hours) despite unrelieved pain (6.63[2.46], 0-10). However, patients receiving the interventions in addition to control care received 46% more analgesia than patients receiving control care alone and had fewer concerns about asking for help and taking analgesia. Changes were not required in the intervention booklet or measures.

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)0.001
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 score0.277
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.018
GPT teacher head0.260
Teacher spread0.241 · 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

Citations30
Published2000
Admission routes2
Has abstractyes

Explore more

Same venuePubMedSame topicPain Management and Opioid UseFrench-language works237,207