Bibliographic record
Abstract
To the Editor: We read with interest the article by Taylor, et al , “Is Chronic Pain a Disease in Its Own Right? Discussions from a Pre-OMERACT 2014 Workshop on Chronic Pain,” published in The Journal of Rheumatology 1. First, we are quite amazed to see how widespread the attendees’ responses were regarding the definition of chronic pain (CP) after a few presentations were given, including 1 proposing that CP should no longer be seen as a symptom. Out of the 5 choices given for the definition of CP, 9% selected “disease,” 26% selected “condition,” 11% selected “syndrome,” 23% selected “symptom complex,” and 31% selected “none of the above.” It is somewhat difficult to accept that only 9% of attendees chose “disease” during the pre-work survey at the workshop, where “Is chronic pain a disease in its own right?” was supposed to be the main objective of this meeting. … Address correspondence to Associate Professor X. Ruan, Anesthesiology, Louisiana State University Health Science Center, 1542 Tulane Ave., New Orleans, Louisiana 70112, USA. E-mail: drxruan88{at}gmail.com
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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.024 | 0.036 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".