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Record W2773880601

Shared decision making in preventive health care: What it is; what it is not.

2017· article· en· W2773880601 on OpenAlexaff
Roland Grad, France Légaré, Bell Nr, James A. Dickinson, Harminder Singh, Moore Ae, Danielle Kasperavicius, Kretschmer Kl

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsMcMaster UniversityResearch Institute in Oncology and HematologyCancerCare ManitobaUniversity of CalgaryMcGill UniversityUniversity of Alberta HospitalUniversity of AlbertaSt. Michael's HospitalCentre for Interdisciplinary Research in RehabilitationMcGill University Health Centre
Fundersnot available
KeywordsProcess (computing)Value (mathematics)Decision-makingComputer scienceMedical decision makingPreventive careHealth careDecision aidsMedicineData scienceAlternative medicineFamily medicineBusiness
DOInot available

Abstract

fetched live from OpenAlex

Shared decision making (SDM) is a process whereby clinicians collaboratively help patients to reach evidence-informed and value-congruent medical decisions. This process is especially relevant in screening for conditions in which there is a close trade-off between harms and benefits. Many screening

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)0.116
metaresearch head score (Gemma)0.161
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.116
Threshold uncertainty score0.614

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1160.161
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0030.003
Science and technology studies0.0090.042
Scholarly communication0.0210.024
Open science0.0040.017
Research integrity0.0120.023
Insufficient payload (model declined to judge)0.0050.001

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 head0.287
GPT teacher head0.467
Teacher spread0.180 · 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 designTheoretical or conceptual
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

Citations109
Published2017
Admission routes1
Has abstractyes

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