The Quality of Written Prescriptions Received at Dental Laboratories in Tabriz, Iran
Bibliographic record
Abstract
AIM: This study was aimed at evaluating the quality of written prescriptions for fixed and removable prosthesis in Tabriz, northwest Iran. METHODS: To assess the quality of written prescriptions for fixed and removable partial dentures, a total of 600 questionnaires were distributed among 15 licensed dental laboratories receiving both fixed and removable prosthesis projects. The technicians were instructed to complete the questionnaires for all the received projects of fixed and removable partial prostheses and the responses were evaluated. RESULTS: Only 9.3% of prescriptions had clearly mentioned the status of disinfection. The patient’s demographic data (age and sex) was noted in 41.3% of written prescription. Information about the shade, ceramic veneering area, and margin design of fixed partial dentures were determined in 82.6%, 16.3%, and 13.3% of cases, respectively. The number of pontics was mentioned in 41% and pontic design in 11% of written prescriptions. For removable partial prostheses, the clasp type and position and connector design were specified in 47.7% and 30.7% of cases, respectively. However, artificial tooth molds were only mentioned in 27.3% of prescriptions. CONCLUSION: Unfortunately, written prescriptions were mostly incomplete which may adversely affect the quality of prosthetic treatment.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".