Diagnostic and treatment methods used by chiropractors: A random sample survey of Canada's English-speaking provinces.
Bibliographic record
Abstract
OBJECTIVE: It is important to understand how chiropractors practice beyond their formal education. The objective of this analysis was to assess the diagnostic and treatment methods used by chiropractors in English-speaking Canadian provinces. METHODS: A questionnaire was created that examined practice patterns amongst chiropractors. This was sent by mail to 749 chiropractors, randomly selected and stratified proportionally across the nine English-speaking Canadian provinces. Participation was voluntary and anonymous. Data were entered into an Excel spreadsheet, and descriptive statistics were calculated. RESULTS: The response rate was 68.0%. Almost all (95.1%) of respondents reported performing differential diagnosis procedures with their new patients; most commonly orthopaedic testing, palpation, history taking, range of motion testing and neurological examination. Palpation and painful joint findings were the most commonly used methods to determine the appropriate joint to apply manipulation. The most common treatment methods were manual joint manipulation/mobilization, stretching and exercise, posture/ergonomic advice and soft-tissue therapies. CONCLUSIONS: Differential diagnosis is a standard part of the assessment of new chiropractic patients in English-speaking Canadian provinces and the most common methods used to determine the site to apply manipulation are consistent with current scientific literature. Patients are treated with a combination of manual and/or manipulative interventions directed towards the joints and/or soft-tissues, as well as exercise instruction and postural/ergonomic advice.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".