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Record W1507484029

Complementary and alternative medicine use among general surgery, hepatobiliary surgery and surgical oncology patients.

2009· article· en· W1507484029 on OpenAlexaff
Colin Schieman, Luke Rudmik, Elijah Dixon, Francis Sutherland, Oliver F. Bathe

Bibliographic record

VenuePubMed · 2009
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineCancerInternal medicineSurgical oncologyDiseaseGeneral surgerySurgery
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.084
GPT teacher head0.309
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2009
Admission routes1
Has abstractyes

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