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Record W2995369433

[THE TIME HAS COME! COMPLEMENTARY MEDICINE IN MEDICAL TRAINING IN ISRAEL].

2019· article· en· W2995369433 on OpenAlexaboutno aff
Menachem Oberbaum, Dorith Shaham, Martine Toledano, Jonathan Halevy, Dina Ben Yehuda

Bibliographic record

VenuePubMed · 2019
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineModalitiesAlternative medicineMedical educationSubject (documents)Health careQuality (philosophy)Patient safetyFamily medicine
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION: The use of complementary and alternative medicine (CAM) has become increasingly popular in most Western countries. As a result, safety-related issues associated with these practices have become more apparent, including adverse effects and negative interactions with conventional medical therapies. The potential dangers associated with CAM are further exacerbated by the absence of high-quality clinical research on the subject; a lack of a basic understanding of the principles of CAM among physicians; and a reluctance of patients to disclose their use of CAM, including those modalities being used in parallel with conventional medical treatments. The increase in CAM use by their patients and the increased potential for harmful effects and interactions are central to the increasing interest among physicians and other healthcare professionals in learning more about CAM paradigms and practices of care. In light of this increasing interest in CAM, the majority of medical schools in the U.S. and Canada and a large number in Europe are providing their students with compulsory or elective courses on this subject. A similar process is taking place in Israel, with the Faculty of Medicine at the Hebrew University in Jerusalem having completed its first compulsory course in CAM at the Hadassah University Medical Center, Ein Karem. The increased use of CAM presents a number of challenges to the conventional medical profession. Medical schools can and should provide students with the knowledge and skills which will enable them to discuss CAM use with their patients, providing evidence-based guidance on the safe and effective use of these modalities.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.160
Threshold uncertainty score0.535

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.1600.037

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 head0.086
GPT teacher head0.321
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreCommentary

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

Citations1
Published2019
Admission routes1
Has abstractyes

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