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Minimal important difference estimates for patient-reported outcomes: A systematic survey

2020· review· en· W3111011457 on OpenAlexafffund
Alonso Carrasco‐Labra, Tahira Devji, Anila Qasim, Mark Phillips, Yuting Wang, Bradley C. Johnston, Niveditha Devasenapathy, Dena Zeraatkar, Meha Bhatt, Xuejing Jin, Romina Brignardello‐Petersen, Olivia Urquhart, Farid Foroutan, Stefan Schandelmaier, Héctor Pardo‐Hernández, Qiukui Hao, Vanessa Wong, Zhikang Ye, Liam Yao, Robin W.M. Vernooij, Hsiaomin Huang, Linan Zeng, Yamna Rizwan, Reed Siemieniuk, Lyubov Lytvyn, Donald L. Patrick, Shanil Ebrahim, Toshi A. Furukawa, Gihad Nesrallah, Holger J. Schünemann, Mohit Bhandari, Lehana Thabane, Gordon Guyatt

Bibliographic record

VenueJournal of Clinical Epidemiology · 2020
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsCanada Research ChairsUniversity of GuelphUniversity of AlbertaHumber River Regional HospitalMcMaster UniversityImpactTed Rogers Centre for Heart ResearchUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health Research
KeywordsMedicineMEDLINE

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)0.044
metaresearch head score (Gemma)0.161
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.956
Threshold uncertainty score0.231

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.161
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0130.016
Bibliometrics0.0050.004
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0030.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.859
GPT teacher head0.635
Teacher spread0.225 · 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 designSystematic review
DomainMethods
GenreReview

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

Citations124
Published2020
Admission routes2
Has abstractno

Explore more

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