Bibliographic record
Abstract
To the Editor: Littlejohn and Guymer1 attempt to reestablish the nexus between injury and the fibromyalgia syndrome (FMS). Their editorial recommends that “the most important thing is to get the diagnosis right”. However, their suggestion that the diagnosis of whiplash is an “emotionally charged” term holds no credibility if they suggest that “fibromyalgia” should be preferred. Central sensitization has become the first explanatory resort of those seeking to describe virtually any issue relating to chronic pain and the alleged behavioral consequences of mood disturbance and disability. Absent from this discourse is the fact that central sensitization is a reversible phenomenon in virtually all experimental models where an injury occurs as a discrete event. There is no need for epidemiological series to demonstrate this. In competitive contact sports, the full spectrum of physical injuries is seen. Occasionally, serious and permanent disability occurs — almost invariably because of major neurological injury. Despite this, the FM signal remains entirely absent from this domain. The authors assert … Address correspondence to Dr. D. Kapur, Flinders University, School of Medicine, Flinders Drive, Bedford Park, Adelaide, South Australia 5042, Australia. E-mail: dilip.kapur{at}flinders.edu.au, dkapur{at}chg.net.au
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 | 0.002 | 0.015 |
| 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 | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.026 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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.
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".