Managing chronic pain in the non-specialist setting: a new SIGN guideline
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 …
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it