MétaCan
Menu
Back to cohort

The Current Status of the Anatomical Sciences Curriculum in U.S. and Canadian Dental Schools

2003· article· en· W2181963023 on OpenAlexaboutno aff
Geoffrey D. Guttmann

Bibliographic record

VenueJournal of Dental Education · 2003
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumSyllabusMedical educationDental educationThe InternetGross anatomyPsychologyMedicineMathematics educationPedagogyAnatomyComputer science

Abstract

fetched live from OpenAlex

The anatomical sciences form one of the major building blocks of the basic medical sciences in the professional training of dentists. This paper defines the courses and classifies the formats of teaching for each course within the anatomical sciences curriculum. Information was gathered from the Internet, specifically the American Dental Education Association (ADEA) website links to U.S. and Canadian dental schools and their online catalogues or bulletins as well as online course syllabi. The results demonstrate the distribution of schools in the United States and Canada teaching anatomical sciences in the following categories: stand-alone, sequential, and multifaceted courses for gross anatomy; stand-alone and integrated courses for histology; stand-alone, integrated, incorporated, and no course for neuroanatomy; and stand-alone, incorporated, and no course in embryology. This paper concludes with the proposition that a survey of the usage of anatomical knowledge in use in a typical dental general practice needs to be conducted. The results of such a survey need to be evaluated with the intention of determining what should be taught in a dental clinical anatomical sciences curriculum.

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)0.004
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.963
Threshold uncertainty score0.272

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.010
Science and technology studies0.0030.003
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.009
GPT teacher head0.342
Teacher spread0.333 · 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 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

Citations21
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Dental EducationSame topicInnovations in Medical EducationFrench-language works237,207