The participation of Ontario oral and maxillofacial surgeons in oral, lip and oropharyngeal cancer.
Bibliographic record
Abstract
BACKGROUND: Oral, lip and oropharyngeal cancer accounts for up to 75% of head and neck cancers. Dental professionals contribute to improved treatment outcomes through early detection of these cancers. Oral and maxillofacial surgeons (OMFS) are trained to participate in numerous phases of care for patients with oral, lip and oropharyngeal cancer. OBJECTIVE: To quantify the participation of Ontario OMFS in various phases of oral, lip and oropharyngeal cancer care. METHODS: A survey assessing participation of Ontario OMFS in screening, education, prevention, diagnosis, surgical oncology, reconstruction and rehabilitation of patients with oral, lip and oropharyngeal cancer was conducted in January and February 2013. RESULTS: Of the 210 OMFS registered with the Royal College of Dental Surgeons of Ontario, 191 were contacted, and 95 (49.7%) responded to the survey. Of the respondents, 98.9% were involved in cancer screening, 96.8% were involved in prevention and early intervention (monitoring and treatment) of premalignant lesions and 94.7% participated in diagnosis and staging. Early stage oral, lip and oropharyngeal cancer was managed surgically by 44.1% of the respondents, while 6.4% managed late-stage disease. Oral rehabilitation was managed by 77.7% of respondents. CONCLUSION: OMFS are an integral part of all phases of oral and oropharyngeal cancer care including primary surgical oncology in Ontario. Dental professionals can help improve outcomes of this care through early identification of cancer using surveillance examinations at all routine dental visits. This early detection contributes directly to disease-free survival and quality of life.
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 | 0.001 | 0.004 |
| 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) | 0.007 | 0.001 |
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".