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Record W4234647661 · doi:10.3899/jrheum.160991

Dr. Fitzcharles, <i>et al,</i> reply

2016· letter· fa· W4234647661 on OpenAlexafffundvenueabout
Mary‐Ann Fitzcharles, Peter A. Ste‐Marie, Emmanouil Rampakakis, John S. Sampalis, Yoram Shir

Bibliographic record

VenueThe Journal of Rheumatology · 2016
Typeletter
Languagefa
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsMcGill UniversityJewish General HospitalMcGill University Health Centre
FundersJewish General HospitalMcGill University Health CentreMcGill University
KeywordsMedicineCohortExaggerationFibromyalgiaPaymentFamily medicinePhysical therapyGerontologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

To the Editor: We appreciate the comments of Drs. Ferrari and Russell1 regarding our study that examined factors associated with disability in persons with fibromyalgia (FM)2. We observed that 31% of patients with FM in our cohort of 248 patients were disabled and receiving disability payments for FM. The disabled patients with FM were more symptomatic, used more medications, and were more likely to have previously worked in physically demanding jobs. Drs. Ferrari and Russell raise the issue of symptom exaggeration by some persons with FM as a means of obtaining disability payments and suggest that effort testing and … Address correspondence to M.A. Fitzcharles, Montreal General Hospital, 1650 Cedar Ave., Montreal H3G 1A4, Quebec, Canada. E-mail: mary-ann.fitzcharles{at}muhc.mcgill.ca

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)0.002
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.024
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0240.025
Insufficient payload (model declined to judge)0.0060.006

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 head0.025
GPT teacher head0.305
Teacher spread0.280 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2016
Admission routes4
Has abstractyes

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