MétaCan
Menu
Back to cohort
Record W3023900372 · doi:10.1177/1753193420922791

Direct and indirect utilities of patients with mild to moderate versus severe carpal tunnel syndrome

2020· article· en· W3023900372 on OpenAlexaff
Annie M. Q. Wang, Helene Retrouvey, Murray Krahn, Steven J. McCabe, Heather L. Baltzer

Bibliographic record

VenueJournal of Hand Surgery (European Volume) · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsCarpal tunnel syndromeMedicineVisual analogue scaleCohortPhysical therapyRetrospective cohort studyCohort studyDimension (graph theory)Carpal tunnelSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Health utility is a quantitative global measure of patients’ health status. This retrospective cohort study aimed to compare health utilities of patients with mild to moderate versus severe carpal tunnel syndrome and determine inter-instrumental agreement. Health utilities of 29 patients with varying severity of carpal tunnel syndrome were measured indirectly by Short-Form Sixth Dimension and EuroQol 5D questionnaire and directly by Chained Standard Gamble and a visual analogue scale. Health utility was 0.69 for Short-Form Sixth Dimension, 0.78 for EuroQol 5D Questionnaire, 0.98 for Chained Standard Gamble, and 0.76 for the visual analogue scale. There was a significant inter-instrumental agreement between three of the instruments, but not the Chained Standard Gamble. The difference in health utilities between patients with mild or moderate versus severe carpal tunnel syndrome was significant only for the EuroQol 5D questionnaire. We conclude based on our results that there are no clear indications on how health utilities can be integrated into decision analysis models and economic evaluation regarding carpal tunnel syndrome of various severities . Level of evidence: IV

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.216
GPT teacher head0.310
Teacher spread0.094 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Hand Surgery (European Volume)Same topicHealth Systems, Economic Evaluations, Quality of LifeFrench-language works237,207