Bibliographic record
Abstract
The effectiveness of chiropractic spinal manipulation for back pain is uncertain Sports medicine clinicians with varied training include joint mobilisation and manipulation among their therapeutic skills. Examples include chiropractors, physiotherapists, and osteopaths, not to mention the doctors and massage therapists who treat various joint pathologies. Although athletes rarely have osteoporosis, the broad field of sports medicine includes the use of exercise therapies and treatment of the musculoskeletal system in people of all ages. Therefore this leader focuses on the role of chiropractic joint manipulation. Back pain sufferers from more than 60 countries consult chiropractors.1 A booklet by the British Chiropractic Association boldly states that “95% of back pain is mechanical in origin, and can be treated by a chiropractor in a primary care setting”.2 Yet there are many who doubt such promotional statements. A recent, perhaps more sober, assessment of the data reads differently: “43 randomised trials of spinal manipulation for treatment of acute, subacute and chronic low back pain have been published. 30 favoured manipulation over the comparison treatment in at least a subgroup of patients and the other 13 found no significant differences”.3 However, these trials used mostly non-chiropractic spinal manipulation. The only systematic review of exclusively chiropractic spinal manipulation concluded that “the available RCTs provided no convincing evidence of the effectiveness of chiropractic for acute or chronic low back pain”.4 Since the publication of this article, the emerging trial data have not tended to be encouraging. The effectiveness of chiropractic spinal manipulation for back pain is …
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.009 |
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".