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Record W1987871443 · doi:10.3138/jvme.31.4.380

Use of a Non-traditional University Ambulatory Practice to Teach Large Animal Medicine

2004· article· en· W1987871443 on OpenAlexvenueno aff
Margaret A. Masterson, B. Welker, Lowell T. Midla, Richard W. Meiring, Kent H. Hoblet

Bibliographic record

VenueJournal of Veterinary Medical Education · 2004
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAmbulatoryPrivate practiceMedicineMedical educationVeterinary medicineFamily medicineSurgery

Abstract

fetched live from OpenAlex

While many other veterinary schools have moved away from a traditional university-based ambulatory practice, the Ohio State University's Large Animal Practice has continued to provide a cost-effective and valuable method of preparing students for today's careers in veterinary medicine. The practice provides a full array of services to production, equine, and camelid clients, including herd health, individual animal medicine and surgery, and emergency services. Acquiring established practices from alumni has formed the client base. Four full-time veterinarians operate the clinic. While these same clinicians do some classroom teaching, their primary responsibility is devoted to the five to six fourth-year veterinary students who rotate through the clinic every two weeks. Teaching methods and objectives for these students include case discussions, homework, truck quiz books, and practice management issues. Financially, the clinic runs as a private practice, with minimal support from the college (201,000 US dollars per fiscal year) and a gross income of 676,000 US dollars per year. Thus, in a cost-effective manner, this required core ambulatory rotation provides students with a scientific learning experience that exposes them to all aspects of large animal production medicine in a real-world setting.

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)0.007
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.430
GPT teacher head0.534
Teacher spread0.104 · 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 designNot applicable
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

Citations6
Published2004
Admission routes1
Has abstractyes

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