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Record W2810638095 · doi:10.3899/jrheum.180043

Dr. Fitzcharles and Dr. Shir reply

2018· letter· en· W2810638095 on OpenAlexaffvenueabout
Mary‐Ann Fitzcharles, Yoram Shir

Bibliographic record

VenueThe Journal of Rheumatology · 2018
Typeletter
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsMcGill UniversityMontreal General Hospital
Fundersnot available
KeywordsMedicineFibromyalgiaArgument (complex analysis)PsychiatryRothschildPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

We appreciate the comments by Dr. Rothschild1 regarding the effect of opioids on adherence to an exercise program in patients with fibromyalgia (FM). Dr. Rothschild has suggested that sleep dysfunction due to opioids may be an important factor to explain poor adherence to exercise recommendations. The argument that opioids could accentuate the sleep disorder in persons with FM is correct. Whether sleep disorder per se , by whatever mechanism, is the unique reason to explain both the findings of the study by Kim and colleagues, as … Address correspondence to M.A. Fitzcharles, Montreal General Hospital, 1650 Cedar Ave., Montreal, Quebec H3G 1A4, 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)0.025
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: Editorial · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0030.002
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0220.025
Insufficient payload (model declined to judge)0.0090.005

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 head0.029
GPT teacher head0.302
Teacher spread0.273 · 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
GenreEditorial

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

Citations2
Published2018
Admission routes3
Has abstractyes

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