A multi‐centre study to explore the feasibility and acceptability of collecting data for complex regional pain syndrome clinical studies using a core measurement set: Study protocol
Bibliographic record
Abstract
OBJECTIVES: This international, multicentre study will inform the final data collection tools and processes which will comprise the first international, clinical research registry for complex regional pain syndrome (CRPS). This study will: (a) test the feasibility and acceptability of collecting outcome measurement data using a patient reported, questionnaire core measurement set (COMPACT [Core Outcome Measurement set for complex regional PAin syndrome Clinical sTudies]); and (b) test and refine an electronic data management system to collect and manage the data. METHODS: A maximum of 240 adults, meeting the Budapest diagnostic clinical criteria for CRPS type I or II, will be recruited across eight countries. The COMPACT questionnaire will be completed on two occasions: at baseline (T1) and 6 months later (T2). At T2, participants will choose to complete COMPACT using a paper or electronic version. Participants will be asked to feed back on their experience of completing COMPACT via a questionnaire. A separate questionnaire will ask clinicians to feed back their experience of data collection. ANALYSIS: The study is not aiming to derive statistically significant data but to ascertain the practicalities of collecting data, using the COMPACT questionnaire set, across a range of different cultures and populations. At the end of the study, a single workshop will be convened to review the findings and agree the final documents and processes for the international registry.
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 | 0.145 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".