MétaCan
Menu
Back to cohort
Record W4360603523 · doi:10.1007/s12630-023-02432-3

Engaging patients in anesthesiology research: a rewarding frontier

2023· article· en· W4360603523 on OpenAlexafffund
Michael Verret, Dean Fergusson, Stuart G. Nicholls, Megan Graham, Fiona Zivkovic, Maxime Lê, Allison Geist, Nhat Hung Lam, Ian D. Graham, Alexis F. Turgeon, Daniel I. McIsaac, Manoj M. Lalu

Bibliographic record

VenueCanadian Journal of Anesthesia/Journal canadien d anesthésie · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsThe Quebec Population Health Research NetworkOttawa HospitalUniversité LavalUniversity of OttawaSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsAnesthesiologyFrontierPain medicineMedicineMedical educationPsychologyAnesthesiaPolitical science

Abstract

fetched live from OpenAlex

Patient-oriented research in anesthesiologyPatient-oriented research is an emerging approach that engages patients, clinicians, and researchers to ensure focus on patient-identified priorities. 1 From a clinical perspective, involving patients in medical decisions is required by law and professional code (i.e., informed consent).Informed consent requires evidence that is pertinent to the decision at hand, but also information that is deemed relevant by the decision maker (the patient).Despite the importance of research in providing evidence to inform such clinical decisions, engaging patients in clinical research is not frequently done, is often suboptimal, and is not required by regulations and good practice.The failure to engage patients in clinical research has led to large swathes of research that neither addresses

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)0.091
metaresearch head score (Gemma)0.173
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.909
Threshold uncertainty score0.482

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0910.173
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0170.030
Scholarly communication0.0300.021
Open science0.0050.029
Research integrity0.0190.032
Insufficient payload (model declined to judge)0.0320.008

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 head0.179
GPT teacher head0.385
Teacher spread0.206 · 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.

Study designNot applicable
DomainMethods
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

Citations10
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Anesthesia/Journal canadien d anesthésieSame topicMental Health and Patient InvolvementFrench-language works237,207