Clinical decision‐making in restorative dentistry. Content‐analysis of diagnostic thinking processes and concurrent concepts used in an educational environment
Bibliographic record
Abstract
CONTEXT: Dental practice is affected by variation in diagnosis and treatment planning between clinicians. The process underlying the decisions leading to management of dental caries is poorly understood. OBJECTIVE: To explore diagnostic thinking and identify information used in the diagnosis, management and treatment of caries. SUBJECTS/MATERIALS: 15 senior dental students (mean age, 23.1 years; 66% female) in Mexico City were interviewed using a Simulated Patient Model (SPM). METHODS: The SPM consultation session and its review, aimed at eliciting an introspective recall description of the Diagnostic Thinking Processes (DTP) and the pieces of information (concepts) employed, were transcribed and content-analyzed (CA). CA reproducibility coefficients were 0.53 to 0.64. The cognitive psychology Gale & Marsden (GM) model was used to structure DTP and concepts. Data were analyzed with chi-squared tests. RESULTS: DTP utilization was similar to the original report in endocrinology and neurology cases. A wide variety of concepts were combined with DTP to describe the strategies and information preferentially resorted to while addressing four areas identified in the consultation. CONCLUSIONS: Narrow arrays of concepts of restorative concern (mostly physical features present in/on teeth) were often investigated using DTP 1, 2 and 4 in combination with the use of dental explorers. The heuristics detected link features common to the GM model and to an indirect pattern-recognition model, whereby reliance on visual/tactile concepts facilitates the acquisition of a clinically meaningful image. Non-clinical factors believed to be at play in the application of strategies were not investigated but were controlled for in the SPM.
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 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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".