A Measure of Agreement Between Clinicians and a Computer‐Based Decision Support System for Planning Dental Treatment
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.061 | 0.228 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".