Bibliographic record
Abstract
The symptoms of “fibromyalgia” are due to the interaction between referred pain and amplifying factors. Given that Moldofsky and I described pain amplification related to measurable sleep disturbances1, we yield to no one in recognition of neural factors. Missing from Shleyfer, et al 2 published in a recent issue of The Journal , from 2 commentaries in the current issue3,4, and from the long Wikipedia entry5 is any discussion of referred pain and of tender sites as markers of referred pain. These help identify the underlying somatic pathology, and therefore the necessary treatment and research strategies. Labeling alone doesn’t help; for a full diagnosis, definition of underlying problems and appropriate treatment strategies are required. But there need be no talk of “war” among friends and colleagues (everybody loses wars). Disagreement should be the beginning of creative discussion. The scientific study of referred pain began with the work of J.H. Kellgren6, with whom I had the privilege of working. I quote from a letter I received from him dated May 28, 1999, about 2 years before his death: “...When I started the experimental pain studies in 1936, it was believed that pain was accurately localized in all somatic structures and that only viscera gave referred pain through some special reflex. Our work at that time showed this to be false and led to a clinical method for ascertaining the anatomical source of all pains. This had a big impact on clinical diagnosis in medicine and surgery as well as rheumatology but has recently lost its interest since all the scanners and other machines have taken over....” With great respect, I submit he overstated his case. When I trained, the work of Thomas Lewis and Kellgren was very fresh. We recognized certain features …
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.057 | 0.019 |
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".