Managing chronic pain in the non-specialist setting: a new SIGN guideline
Bibliographic record
Abstract
Chronic pain, defined as pain lasting beyond normal tissue healing time (taken to be 3 months),1 is a syndrome that affects a large proportion of the primary care population. It is ‘significant’ in around 14% of UK adults, imposing a heavy burden on the physical and psychosocial health of sufferers, their families and society, at high cost to the healthcare services.2 It was estimated in 2002 that people with chronic pain account for 4.6 million GP appointments in the UK, at an annual cost to the NHS of £69 million, equivalent to the employment of 793 GPs.3 Although many clinical conditions can lead to chronic pain, there are common underlying neurobiological and psychosocial mechanisms, and the impact is generally independent of the clinical aetiology. Effective assessment and treatment of chronic pain therefore means that GPs should have: Unfortunately, none of these requirements is generally in place. Undergraduate training in management of pain is demonstrably minimal, accounting for <1% of programme hours,4 despite its high prevalence and impact. Much of the available evidence for potential interventions is derived from specialist settings or in specific clinical conditions, making it difficult to apply to a general primary care population. Even standard treatments, such as drugs, often lack evidence for effectiveness …
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".