A RETROSPECTIVE ANALYSIS OF REFERRAL PATTERNS TO A UNIVERSITY ORAL MEDICINE CLINIC
Bibliographic record
Abstract
Objectives: To assess, characterize and analyze, referral patterns to an Oral Medicine Clinic at the University of Alberta. Emphasis was placed on assessing the types of referrals made by dental and medical practitioners, as well as access to care issues that patients face when receiving Oral Medicine specialty care. Materials and Methods: A retrospective chart review of all Oral Medicine/Oral Pathology specialists at the University of Alberta for the year 2015 was performed. Method analysis: Proportions for data points were collected using a 95% Wilson Score Confidence Interval. Two-sided Fisher’s Exact tests were performed to assess for statistical differences between data when relevant. Results: 924 patients were included in the analysis. Dental practitioners referred cases most frequently (81.4%) with general dentists representing the largest total proportion (74.5%). White/red lesions were the most common reason for referrals (38.0%), with the tongue (21.8%) and gingiva (17.6%) representing the most common locations of issues. There was no significant difference between the accuracy of provisional diagnoses between physicians and dentists, although dentists referred cases urgently more frequently (16.9% of dentist vs 7.0% of physician cases). The experience of dentists did not have any effect on accuracy of provisional diagnoses, however it did affect the type of conditions referred. Immune mediated conditions were the most common final diagnosis, which were 28.7% of cases. The average wait time for patients was 105.5 days. The average distance travelled by patients was 55.44 km. 18.7% of urgent referrals were seen within 2 weeks. Conclusions: Patients often travel long distances and experience extended wait times after referral. There are small differences between the referral patterns of dental and medical practitioners, but increased training and continuing professional development would benefit both groups. This data can be used to develop future curricula for dental students and can aid in developing CE courses for graduated dentists. In summary, this research highlights the need for improvement of access to Oral Medicine care by patients in Edmonton, Alberta.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".