Back and neck pain: in support of routine delivery of non-pharmacologic treatments as a way to improve individual and population health
Bibliographic record
Abstract
Chronic back and neck pain are highly prevalent conditions that are among the largest drivers of physical disability and cost in the world. Recent clinical practice guidelines recommend use of non-pharmacologic treatments to decrease pain and improve physical function for individuals with back and neck pain. However, delivery of these treatments remains a challenge because common care delivery models for back and neck pain incentivize treatments that are not in the best interests of patients, the overall health system, or society. This narrative review focuses on the need to increase use of non-pharmacologic treatment as part of routine care for back and neck pain. First, we present the evidence base and summarize recommendations from clinical practice guidelines regarding non-pharmacologic treatments. Second, we characterize current use patterns for non-pharmacologic treatments and identify potential barriers to their delivery. Addressing these barriers will require coordinated efforts from multiple stakeholders to prioritize evidence-based non-pharmacologic treatment approaches over low value care for back and neck pain. These stakeholders include patients, health care providers, health care organizations, administrators, payers, policymakers and researchers.
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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 | 0.020 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 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".