Prevalence of nonmusculoskeletal complaints in chiropractic practice: Report from a practice-based research program
Bibliographic record
Abstract
Objective: To identify patient and practice characteristics that might contribute to people's seeking chiropractic care for nonmusculoskeletal complaints. Design: This was a cross-sectional study conducted through the methods of practice-based research. Setting: Data were collected in 1998-1999 in chiropractic offices in the United States, Canada, and Australia; data were managed by a practice-based research office operating in a chiropractic research center. Population: The subjects were new and established patients of all ages who visited the participating offices during a designated data collection week. Data Analysis: Multiple logistic regression was used to examine factors associated with patients' presenting for nonmusculoskeletal chief complaints. Pearson's X 2 test was used to examine associations among practice variables and the proportion of patients with nonmusculoskeletal chief complaints. Results: A total of 7651 patients of 161 chiropractors in 110 practices in 32 states and 2 Canadian provinces participated; data from 2 Australian practices were included in the totals but not in the analysis. Nonmusculoskeletal complaints accounted for 10.3% of the chief complaints. The following characteristics made patients more likely to present with nonmusculoskeletal chief complaints: being less than 14 years of age (adjusted odds ratio [AOR], 6.9; 95% CI, 5.2-9.1); being female (AOR, 1.5; CI, 1.3-1.8); presenting in a small town/rural location (AOR, 1.9; CI, 1.3-2.7); reporting more than 1 complaint, especially nonmusculoskeletal complaints (AOR, 4.9; CI, 3.9-6.0); having received medical care for the chief complaint (AOR, 3.4; CI, 2.9-4.1); and having first received chiropractic care before 1960 (AOR, 1.7; CI, 1.1-2.4). Practices with the highest proportion of patients with nonmusculoskeletal chief complaints (>17%) were less likely to accept insurance and more likely to be in locations with populations greater than 100,000. They used the most common chiropractic adjustive techniques less frequently and used more nonadjustive procedures, especially diet/nutrition counseling, nutritional supplementation, herbal preparations, naturopathy, and homeopathy. Conclusions: Drawing on practices with the patient and practice characteristics identified in this study to conduct outcomes studies on nonmusculoskeletal conditions is a possible direction for future research.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".