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Record W4298289238

Doctors of osteopathic medicine (DO): a Canadian perspective

2014· article· en· W4298289238 on OpenAlexaffabout
Sevan Evren, Andrew Yuzhong Bi, Shuchi Talwar, Andrew Yeh, Howard S. Teitelbaum

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2014
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal Disorders and Rehabilitation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPerspective (graphical)Osteopathic medicine in the United StatesMedicineAlternative medicineFamily medicinePsychologyMedical educationComputer sciencePathologyArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Background: Doctors of osteopathic medicine (DO) are one of the fastest growing segments of health care professionals in the United States. Although Canada has taken significant leaps in the acknowledgment of US trained DOs, there continues to be a lack of understanding of the profession by Canadian trained physicians. In this article, we provide a brief overview of osteopathic medical education and training in the United States. Method: Current information of osteopathic training by American Association of Colleges of Osteopathic Medicine (AACOM) and American Osteopathic Association (AOA) was presented. Data pertaining to Canadians enrolled in osteopathic colleges was compared with allopathic (MD) and international medical graduates (IMGs). Results: Doctors of osteopathic medicine programs provide an additional pathway for students interested in pursuing a medical education. Canadian applications to osteopathic colleges are expected to grow due to successful post-graduate US residency matching, increased difficulty of matriculating at Canadian medical schools, and a greater awareness of the profession in Canada. Conclusions: Given the increasing enrollment of Canadian students in US osteopathic medical schools, we expect that Canadian DOs will play a significant role in shaping health care in both the US and Canada.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.000

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 head0.136
GPT teacher head0.551
Teacher spread0.415 · 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.

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

Citations0
Published2014
Admission routes2
Has abstractyes

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