Use of complementary and alternative medical therapies for chronic rhinosinusitis: a canadian perspective.
Bibliographic record
Abstract
BACKGROUND: Many Canadians use complementary and alternative medicines (CAMs) to treat their chronic diseases. The objective of this study was to report patients' use of CAM for chronic rhinosinusitis (CRS) and to determine factors predictive of CAM use. METHOD: A cross-sectional survey was conducted. Self-report questionnaires were administered to patients with CRS using strict inclusion and exclusion criteria. The questionnaire included demographic information, questions pertaining to disease severity, and CAM use for CRS treatment. Statistical analysis was used to compare gender, age range, symptom duration, pharmacotherapy use, and surgical frequency among CAM users and nonusers. A binomial logistic regression model was developed to predict CAM use. Secondary outcome measures included factors predictive of CAM use, type of CAM used, and reasons for using CAM. RESULTS: Data were obtained from 288 patients. Forty-five respondents (15.6%) had used CAM as a treatment for their CRS. CAM users were more likely to be females and more likely to have used each class of pharmacotherapy. On logistic regression, female gender and use of nasal corticosteroids were predictive of CAM use. CONCLUSION: The use of CAM as treatment of CRS is common. Females and those who have used the various classes of pharmacotherapy are more likely to use CAM. Both female gender and nasal corticosteroid use are predictive of CAM use. Physicians should routinely inquire about CAM use from their patients with CRS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".