FINDINGS AFTER EXPERIENCE WITH AN ONLINE RESOURCE FOR RESEARCHERS WORKING IN CHRONIC PAIN
Bibliographic record
Abstract
Background and aim u2022tThere is a broad spectrum of chronic pain conditions, many with a high disease burden and no adequate standard of care.u2022tThe aim of this initiative is to provide a comprehensive and single point of reference for those chronic pain conditions with the highest burden of disease and unmet medical needs, to increase knowledge and encourage cross-team working.Methodsu2022tSearch strings were conducted using the most relevant publication data bases: Pubmed, Google Scholar and Embaseu2022tStructured interviews were conducted with more than 50 physicians across 7 European countries.u2022tIndications were selected as those with a high burden of disease and (perceived) knowledge limitation indexu2019.u2022tThe McGill pain score was utilised in an analysis of the selected conditions; where this was not available, correlation between McGill and NRS scores were estimated. Resultsu2022tMore than 500 conditions with pain as a key feature were identified, but only a few selected for different reasonsot80 pain indications were placed for development into presentation modules following a standard format, including pathophysiology, clinical presentation, therapy, targets under research, unmet needsu2026Conclusionsu2022tA high disease burden and unmet needs are apparent across many chronic pain conditions.u2022tDifferences but also, similarities have shown evident in different pain indications.u2022tA strategy is being implemented to try to find the biological basis for those findingsu2022tThe comprehensive repository is available online to pain researchers willing to join efforts to this initiative
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 | 0.075 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.053 | 0.022 |
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".