Use of complementary and alternative medications among patients in an obstetrics and gynecology clinic.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the current use of complementary and alternative medication (CAM) products among women in obstetrics and gynecology outpatient clinics. STUDY DESIGN: This study was performed at a major academic center among patients seen at either a faculty-led private clinical practice site (n = 250) or a resident-led clinical practice site (n = 250). Patients were requested to bring a written list and the medication bottles (prescriptions, over-the-counter medications and CAM products) to the clinic, where a survey was then administered. RESULTS: Overall, 18.6% of participants were using CAM products. Significantly more patients reported using CAM products in the faculty private practice as compared to the resident clinic practice (28.4% vs. 8.8%, respectively, p value < 0.05). Only 29.0% of CAM products users had spoken to a healthcare provider regarding CAM products. Multivariate logistic regression model determined that older age (p < 0.0001) and Caucasian ethnicity (p = 0.0245) were associated with higher rates of CAM products use. CONCLUSION: In this study CAM products use was not as prevalent as anticipated for this patient population, however it continues to be underreported to providers. Healthcare professionals should continue to increase their knowledge about CAM products, take a proactive role to improve documentation, and develop an open communication with patients regarding appropriate use of CAM products.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".