Chronic noncancer pain management
Bibliographic record
Abstract
PROBLEM ADDRESSED: Chronic noncancer pain is often excessively managed with medications (most notably opioids) instead of nonpharmacologic options or multidisciplinary care-the gold standards. OBJECTIVE OF PROGRAM: To offer an effective alternative to pharmacologic management of chronic noncancer pain in primary care. PROGRAM DESCRIPTION: Patients 18 years of age or older with chronic noncancer pain were referred by family physicians or nurse practitioners in a family health team (outpatient, multidisciplinary clinic) in Ottawa, Ont. A registered nurse used the Pain Explanation and Treatment Diagram with patients, taught self-management skills (related to habits [smoking, consumption of alcohol, diet], exercise, sleep, ergonomics, and psychosocial factors), and referred patients to relevant resources. CONCLUSION: A nurse-led chronic pain program, initiated without extra funding, was successfully integrated into a primary care setting. Among the participating patients in the pilot project, outcomes related to pain intensity, pain interference with daily living, and opioid use were encouraging. This program could serve as a model for improving chronic noncancer pain management in primary care.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".