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Montreal Accord on Patient-Reported Outcomes (PROs) use series–Paper 7: modern perspectives of measurement validation emphasize justification of inferences based on patient reported outcome scores

2016· article· en· W2564797893 on OpenAlexafffundabout
Richard Sawatzky, Eric K. H. Chan, Bruno D. Zumbo, Sara Ahmed, Susan J. Bartlett, Clifton O. Bingham, William Gardner, Jeffrey W. Jutai, Ayse Kuspinar, Tolulope T. Sajobi, Lisa M. Lix

Bibliographic record

VenueJournal of Clinical Epidemiology · 2016
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of ManitobaMcGill UniversityUniversity of OttawaUniversity of CalgaryChildren's Hospital of Eastern OntarioUniversity of British ColumbiaProvidence Health CareMcMaster UniversityTrinity Western UniversityCentre for Interdisciplinary Research in RehabilitationCentre for Advancing Health OutcomesWestern University
FundersCanada Research ChairsResearch Manitoba
KeywordsOutcome (game theory)Series (stratigraphy)Patient-reported outcomeMedicinePsychologyQuality of life (healthcare)EconomicsNursingMathematical economics

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)0.200
metaresearch head score (Gemma)0.372
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.800
Threshold uncertainty score0.987

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2000.372
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0070.007
Science and technology studies0.0030.004
Scholarly communication0.0040.003
Open science0.0060.004
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0030.002

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 head0.343
GPT teacher head0.457
Teacher spread0.113 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
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

Citations33
Published2016
Admission routes3
Has abstractno

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