Bibliographic record
Abstract
To the Editor: We thank Dr. Sarkozi for his response to our editorial entitled “Should rheumatologists retain ownership of fibromyalgia?”1. We had hoped that our comments would stimulate thought and discussion on the issue of optimal care for patients with fibromyalgia (FM). The issue of debate concerns our differing opinions regarding the choice of healthcare professional that is best suited to provide care for patients with FM. Dr. Sarkozi believes rheumatologists should continue to manage patients with FM, based on a personal opinion that all FM originates as a result of generalized osteoarthritis (OA) or soft tissue rheumatism. This simplistic view of causality is put forward to rationalize the choice of the ideal treating physician. We reemphasize that the essence of our message is to examine strategies for the total care of these patients, who in addition to pain, have mood disorder, sleep abnormality, fatigue, and a multiplicity of other somatic symptoms. Therefore successful management of FM can only be achieved if all components of the syndrome are addressed, many of which are outside the scope of usual rheumatology practice. … Address correspondence to Dr. M-A. Fitzcharles, Montreal General Hospital, 1650 Cedar Avenue, 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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.027 | 0.032 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".