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Record W2329865255 · doi:10.15766/mep_2374-8265.8007

Introduction to Cast Removable Partial Dentures and Surveying

2010· article· en· W2329865255 on OpenAlexaff
Cecilia Dong

Bibliographic record

VenueMedEdPORTAL · 2010
Typearticle
Languageen
FieldDentistry
TopicDental Research and COVID-19
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsDenturesDentistryOrthodonticsMedicine

Abstract

fetched live from OpenAlex

Abstract Clear visualization of dental materials and technical procedures demonstrated in real time through videos adds another dimension to student learning that is not available from written/verbal descriptions or still images/teaching aids alone. This module includes a series of 11 short video clips that provide an introduction to the parts of a cast removable partial denture (RPD) and the steps involved in the surveying process. Surveying is an essential skill that dental students need to develop to evaluate and design cast RPDs. Verbal descriptions of surveying in lectures and written descriptions in RPD text books and laboratory manuals are insufficient to prepare second-year dental students learning how to survey a partially edentulous cast for the first time. These videos were produced to fill this gap and have received positive feedback from students and course instructors over the past three years. When students have viewed the videos before the first lab and can follow the videos while completing their first surveying exercise in the lab, they are able to quickly master the criteria used to measure competency in surveying. The videos can also be used to review concepts for students currently in the course and courses for the more advanced dental student.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.020
GPT teacher head0.328
Teacher spread0.308 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2010
Admission routes1
Has abstractyes

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Same venueMedEdPORTALSame topicDental Research and COVID-19French-language works237,207