Treatment procedures and referral patterns of general dentists in Lithuania
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The requirement for dental specialties and the number of specialists in each country depends on the content and execution of undergraduate dental education, the complex oral health care needs of the society and other factors. The aim of our study was to assess specific treatment procedures of Lithuanian general dentists and their need to refer patients to specialists. MATERIALS AND METHODS: Census sampling was employed and the data collected by means of a structured questionnaire asking dentists about the frequency of specific treatment procedures they perform and the frequency of referrals they make to different dental specialists. The results are of a self-reported nature. RESULTS: From general dental practice, 76.3% of cases needing orthodontic treatment were referred to orthodontists. About half of patients needing specialized care were referred to periodontists (50.2%), orthopedists (46.9%) and oral surgeons (45.0). More than one-third (39%) of the cases needing specialist care were referred to endodontists. Only one-third of patients were referred to pediatric dentists. In about 60% of cases needing respective care general dentists extracted teeth and roots, made incisions in acute jaw infections and treated young children; in about half of cases general dentists performed complex endodontic manipulations and treatment with fixed and removable prostheses. CONCLUSIONS: There is a clear need for Lithuanian dental practitioners to refer patients to all types of dental specialists. Undergraduate dental education program and postgraduate training should be more directed toward the extraction of teeth and roots, treatment of young children and provision of dental prostheses to patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".