Bibliographic record
Abstract
OBJECTIVE: To calculate the proportion of care delivered in a chiropractic practice supported by good-quality clinical trials. DESIGN: Retrospective survey. METHODS: Data were collected from patient files relating to 180 consecutive patient visits in a suburban chiropractic practice in northern Spain. Each patient's presenting complaint was paired with the chiropractor's chosen primary intervention. Based on a literature review (Medline, Mantis, and nonautomated searches of local medical libraries), each presenting complaint-primary intervention pairing was categorized according to the level of supporting evidence as follows: Category I, intervention based on good quality clinical trial evidence; Category II, intervention based on poor-quality or no clinical trial evidence. To distinguish between good- and poor-quality clinical trials, studies were critically appraised and assigned quality scores. RESULTS: Of the 180 cases surveyed, 123 (68.3%) (95% CI, 61.5%-75.1%) were based on clinical trials of good methodologic quality (Category I). Only 57 (31.7%) (95% CI, 24.9%-38.5%) of the cases were based on poor-quality or no clinical trial evidence (Category II). CONCLUSIONS: When patients were used as the denominator, the majority of cases in a chiropractic practice were cared for with interventions based on evidence from good-quality, randomized clinical trials. When compared to the many other studies of similar design that have evaluated the extent to which different medical specialties are evidence based, chiropractic practice was found to have the highest proportion of care (68.3%) supported by good-quality experimental evidence.
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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 | 0.064 | 0.194 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".