Research Priorities in the Field of Posttraumatic Pain and Disability: Results of a Transdisciplinary Consensus-Generating Workshop
Bibliographic record
Abstract
Background. Chronic or persistent pain and disability following noncatastrophic "musculoskeletal" (MSK) trauma is a pervasive public health problem. Recent intervention trials have provided little evidence of benefit from several specific treatments for preventing chronic problems. Such findings may appear to argue against formal targeted intervention for MSK traumas. However, these negative findings may reflect a lack of understanding of the causal mechanisms underlying the transition from acute to chronic pain, rendering informed and objective treatment decisions difficult. The Canadian Institutes of Health Research (CIHR) Institute of Musculoskeletal Health and Arthritis (IMHA) has recently identified better understanding of causal mechanisms as one of three priority foci of their most recent strategic plan. Objectives. A 2-day invitation-only active participation workshop was held in March 2015 that included 30 academics, clinicians, and consumers with the purpose of identifying consensus research priorities in the field of trauma-related MSK pain and disability, prediction, and prevention. Methods. Conversations were recorded, explored thematically, and member-checked for accuracy. Results. From the discussions, 13 themes were generated that ranged from a focus on identifying causal mechanisms and models to challenges with funding and patient engagement. Discussion. Novel priorities included the inclusion of consumer groups in research from the early conceptualization and design stages and interdisciplinary longitudinal studies that include evaluation of integrated phenotypes and mechanisms.
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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 | 0.229 | 0.251 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.006 | 0.027 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".