Optimizing chronic pain and disability management
Bibliographic record
Abstract
Chronic non-cancer pain (CNCP) is a complex phenomenon that affects multiple dimensions of daily life. Optimal therapies for managing CNCP must, then, demonstrate clinically important benefits that go beyond reductions in pain and adverse events. The Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) has recommended that clinical trialists who are evaluating treatments for chronic pain consider reporting treatment effects across nine patient-important outcome domains. This thesis begins with an investigation of the extent to which clinical trials evaluating the effects of opioids for CNCP report IMMPACT-recommended core outcome domains. Further, it explores optimal therapeutic strategies for specific CNCP conditions; specifically, it features a systematic review of randomized controlled trials of all pharmacological and non-pharmacological therapies for central post-stroke pain, as well as a plan for a network meta-analysis of all therapies for all chronic neuropathic pain syndromes. Chronic pain is also a common reason for disability, and this thesis concludes with a retrospective cohort study focused on identifying predictors of claim duration following acceptance for disability benefits among Canadian workers.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".