MétaCan
Menu
Back to cohort
Record W3027820216 · doi:10.1136/bmjebm-2020-111371

Problem with patient decision aids

2020· article· en· W3027820216 on OpenAlexaff
Joshua R Zadro, Adrian C. Traeger, Simon Décary, Mary O’Keeffe

Bibliographic record

VenueBMJ evidence-based medicine · 2020
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsDecision aidsCertaintyPresentation (obstetrics)Health careDecision support systemMedicineDecision qualityDecision analysisValue (mathematics)Quality (philosophy)Medical emergencyPatient satisfactionNursingComputer scienceAlternative medicineArtificial intelligenceSurgery

Abstract

fetched live from OpenAlex

Patient decision aids are evidence-based tools designed to help patients make specific and deliberated choices among healthcare options. Research shows that patient decision aids increase knowledge, accuracy of risk perceptions, alignment of care with patient values and preferences, and patient involvement in decision making. Some patient decision aids can reduce the use of invasive and potentially low-value procedures. On this basis, clinical practice guidelines and international organisations have begun to recommend the use of patient decision aids and shared decision making as a strategy to reduce medical overuse. Although patient decision aids hold promise for improving healthcare, there are fundamental issues with patient decision aids that need to be addressed before further progress can be made. The problems with patient decision aids are: (1) Guidelines for developing patient decision aids may not be sufficient to ensure developers select the best available evidence and present it appropriately; (2) Biased presentation of low-certainty evidence is common and (3) Biased presentation of low-certainty evidence is misleading, and could inadvertently support, low-value care. We explore these issues in the article and present a case study of online patient decision aids for musculoskeletal pain. We suggest ways to ensure patient decision aids help patients understand the evidence and, where possible, support high-quality care.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.182
metaresearch head score (Gemma)0.523
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.182
Threshold uncertainty score0.963

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1820.523
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0040.004
Science and technology studies0.0060.015
Scholarly communication0.0130.028
Open science0.0080.012
Research integrity0.0170.029
Insufficient payload (model declined to judge)0.0290.014

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 head0.386
GPT teacher head0.470
Teacher spread0.084 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations19
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueBMJ evidence-based medicineSame topicPatient-Provider Communication in HealthcareFrench-language works237,207