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Montreal Accord on Patient-Reported Outcomes (PROs) use series – Paper 3: patient-reported outcomes can facilitate shared decision-making and guide self-management

2017· article· en· W2606752218 on OpenAlexaffabout
Vanessa K. Noonan, Anne Lyddiatt, Patrick Ware, Susan Jaglal, Richard J. Riopelle, Clifton O. Bingham, Sabrina Figueiredo, Richard Sawatzky, Maria Santana, Susan J. Bartlett, Sara Ahmed

Bibliographic record

VenueJournal of Clinical Epidemiology · 2017
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsCentre de réadaptation Lethbridge-Layton-MackayTrinity Western UniversityProvidence Health CareUniversity of TorontoUniversity of CalgaryMcGill UniversityUniversity of British ColumbiaCentre for Interdisciplinary Research in RehabilitationCentre for Advancing Health OutcomesWestern UniversityPraxis Spinal Cord Institute
Fundersnot available
KeywordsHealth literacyMedicineHealth carePatient participationMedical decision makingDecision aidsMEDLINENursingFamily medicineMedical educationPsychologyAlternative medicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.056
metaresearch head score (Gemma)0.124
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: Methods · Consensus signal: none
Teacher disagreement score0.944
Threshold uncertainty score0.553

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0560.124
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0100.003

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 head0.434
GPT teacher head0.544
Teacher spread0.109 · 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
GenreMethods

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

Citations121
Published2017
Admission routes2
Has abstractno

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