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Record W1848212714 · doi:10.1152/advances.2001.25.2.2

DESIGN AND EVALUATION OF A NATIONAL SET OF LEARNING OBJECTIVES: THE MEDICAL PHYSIOLOGY LEARNING OBJECTIVES PROJECT

2001· article· en· W1848212714 on OpenAlexaboutno aff
Robert G. Carroll

Bibliographic record

VenueAJP Advances in Physiology Education · 2001
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsnot available
Fundersnot available
KeywordsMedical educationSet (abstract data type)PhysiologyHuman physiologyPsychologyMedicineComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

In 1998, the American Physiological Society (APS) and the Association of Chairs of Departments of Physiology (ACDP) began collaboration on a project to develop a set of physiology learning objectives for medical students. Over the next 2 years, more than 50 physiologists collaborated in the development a comprehensive draft containing its 695 learning objectives. Faculty in 31 medical schools in the United States, Canada, and Puerto Rico evaluated these objectives. On the basis of this evaluation, the ACDP recommended deleting 13 of them. The final project, containing 682 objectives, was approved in December 2000 by the ACDP and published on the APS website http://www.the-aps.org/education/MedPhysObj/medcor.htm. The identification of the "content" of medical physiology instruction provides the APS and the ACDP with a tool to introduce emerging topics, such as the physiology of aging and gender differences, at a national level. The medical physiology learning objectives project provides a guide for directing current and future medical physiology instruction in the United States.

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)0.124
metaresearch head score (Gemma)0.158
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.124
Threshold uncertainty score0.654

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1240.158
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.002
Science and technology studies0.0030.003
Scholarly communication0.0040.003
Open science0.0030.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.002

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 head0.018
GPT teacher head0.329
Teacher spread0.310 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations22
Published2001
Admission routes1
Has abstractyes

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