Complementary and alternative medicine use among general surgery, hepatobiliary surgery and surgical oncology patients.
Bibliographic record
Abstract
BACKGROUND: The use of complementary and alternative medicine (CAM) is becoming more common, particularly among cancer patients. We sought to define the frequency of CAM use among general surgery, hepatobiliary and surgical oncology patients and to define some of the determinants of CAM use in patients with benign and malignant disease. METHODS: We asked all patients attending the clinics of 3 hepatobiliary/surgical oncology surgeons from 2002 to 2005 to voluntarily respond on first and subsequent visits to a questionnaire related to the use of CAM. We randomly selected patients for review. RESULTS: We reviewed a total of 490 surveys from 357 patients. Overall CAM use was 27%. There was significantly more CAM use among cancer (34%) versus noncancer patients (21%; p = 0.008), and the use of CAM was more common in patients with unresectable cancer (51%) than resectable cancer (22%; p < 0.001). There was no significant difference in use between men and women. There did not appear to be a change in CAM use with progression of cancer. The most common CAM was herbs or supplements (58% of all users), which were most frequently used by patients with malignant disease. Among the 27 herbs reported to be ingested, 10 are associated with bleeding and hepatotoxicity, as described in the literature. CONCLUSION: Prospective studies evaluating surgical outcomes related to CAM use are needed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".