A demographic and epidemiological study of a Mexican chiropractic college public clinic
Bibliographic record
Abstract
BACKGROUND: Descriptive studies of chiropractic patients are not new, several have been performed in the U.S., Australia, Canada, and Europe. None have been performed in a Latin American country. The purpose of this study is to describe the patients who visited a Mexican chiropractic college public clinic with respect to demographics and clinical characteristics. METHODS: This study was reviewed and approved by the IRB of Parker College of Chiropractic and the Universidad Estatal del Valle de Ecatepec (UNEVE). Five hundred patient files from the UNEVE public clinic from May 2005 to May 2007 were selected from an approximate total number of 3,700. Information was collected for demographics, chief complaints, associated complaints, and previous care sought. RESULTS: The sample comprised 306 (61.2%) female. Most files (44.2%) were in the age range of 40-59 years (mean of 43.4 years). The most frequent complaints were lumbar pain (29.2%) and extremity pain (28.0%), most commonly the knee. Most (62.0%) described their complaints as greater than one year. Trauma (46.6%) was indicated as the initial cause. Mean VAS score was 6.26/10 with 20% rated at 8/10. CONCLUSION: Demographic results compared closer to studies conducted with private clinicians (females within the ages of 40-59). The primary complaint and duration was similar to previous studies (low back pain and chronic), except in this population the cause was usually initiated by trauma. The most striking features were the higher number of extremity complaints and the marked increased level of VAS score (20% rated as 8/10).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".