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A Measure of Agreement Between Clinicians and a Computer‐Based Decision Support System for Planning Dental Treatment

2002· article· en· W2185742531 on OpenAlexaff
Naotsugu Kawahata, Michael I. MacEntee

Bibliographic record

VenueJournal of Dental Education · 2002
Typearticle
Languageen
FieldDecision Sciences
TopicReliability and Agreement in Measurement
Canadian institutionsUniversity of British ColumbiaTRIUMF
Fundersnot available
KeywordsMeasure (data warehouse)Decision support systemMedical physicsMedicinePsychologyComputer scienceArtificial intelligenceData mining

Abstract

fetched live from OpenAlex

This study was conducted to estimate agreement and explain differences between treatment decisions and associated fees recommended by dentists and by a computer-based decision-support program (TxDENT 2.0). The treatment fees associated with forty-eight clinical records of patients attending a dental school clinic provided a measure of correlation and agreement between treatments recommended by TxDENT and by clinical instructors with students. The average difference between the two methods of forecasting fees was $466, and a regression line (y=0.43x+407) with an r-value of 0.54 indicated the strength of the relationship. The differences between methods increased as the cost of treatment increased, due largely to disagreements about the need to restore or replace weak or missing teeth. There is reasonable agreement between TxDENT and the collaborative treatment plans of clinical instructors with their students, which suggests that this computer-based decision-support system for screening patients in a standardized way could be a helpful predictor of treatment provided in a dental school clinic.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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)0.0000.000

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 head0.195
GPT teacher head0.417
Teacher spread0.221 · 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.

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

Citations6
Published2002
Admission routes1
Has abstractyes

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