Periodontal practice and referral profile of general dentists in Nova Scotia, Canada.
Bibliographic record
Abstract
BACKGROUND: In contrast to an expected increase in demand for periodontal services with aging of the population, it appears that referrals to periodontists are declining. OBJECTIVE: To determine the extent to which general dentists in Nova Scotia, Canada, provide periodontal and surgical implant therapies and to determine the factors influencing a dentist's decision to provide treatment or refer patients to a specialist. METHODS: A cross-sectional survey study was performed. The survey questionnaire was mailed to all 443 general dentists practising in Nova Scotia in summer 2009. The questionnaire presented several clinical scenarios and asked respondents whether they would treat the patient in the office or refer to a specialist. The data were analyzed by logistic regression. RESULTS: Of the 279 (63.0%) dentists responding to the survey, 272 (61.4% of the total) were eligible for inclusion in the analysis. The majority of dentists reported rendering nonsurgical periodontal therapy, including scaling (98.5%; 262/266), periodontal maintenance (95.9%; 255/266), and treatment for bruxism (95.1%; 252/265). The most common surgical procedures performed by dentists were frenectomy (29.4%; 78/265), gingivectomy (29.3%; 77/263) and crown-lengthening procedures (17.0%; 46/271). Eleven factors significantly influenced dentists' decisions to treat or refer patients. The most common criteria used in selecting a periodontist were satisfaction of previous patients, previous success with the treatment, and the personality of and good communications with the periodontist. CONCLUSIONS: In this study, dentists reported rendering nonsurgical periodontal therapy on a wide scale, whereas their involvement in oral or periodontal and implant surgical therapies was limited.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".