Bibliographic record
Abstract
INTRODUCTION: Low back pain (LBP) affects about 70% of people in resource-rich countries at some point. Acute low back pain is usually perceived as self-limiting; however, one year later, as many as 33% of people still have moderate-intensity pain and 15% have severe pain. It has a high recurrence rate; 75% of those with a first episode have a recurrence. Although acute episodes may resolve completely, they may also increase in severity and duration over time. METHODS AND OUTCOMES: We conducted a systematic review and aimed to answer the following clinical questions: What are the effects of oral drug treatments for low back pain? What are the effects of local injections for low back pain? What are the effects of non-drug treatments for low back pain? We searched: Medline, Embase, The Cochrane Library, and other important databases up to May 2007 (Clinical Evidence reviews are updated periodically, please check our website for the most up-to-date version of this review). We included harms alerts from relevant organisations such as the US Food and Drug Administration (FDA) and the UK Medicines and Healthcare products Regulatory Agency (MHRA). RESULTS: We found 34 systematic reviews, RCTs, or observational studies that met our inclusion criteria. We performed a GRADE evaluation of the quality of evidence for interventions. CONCLUSIONS: In this systematic review we present information relating to the effectiveness and safety of the following interventions: acupuncture, advice to stay active, analgesics (paracetamol, opioids), back exercises, back schools, bed rest, behavioural therapy, electromyographic biofeedback, epidural corticosteroid injections, lumbar supports, massage, multidisciplinary treatment programmes, muscle relaxants, non-steroidal anti-inflammatory drugs (NSAIDs), spinal manipulation (in the short term), temperature treatments (short wave diathermy, ultrasound, ice, heat), traction, and transcutaneous electrical nerve stimulation (TENS).
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".