The use of panoramic radiographs to address the oral health needs of vulnerable Canadian populations.
Bibliographic record
Abstract
Background: The high demand for oral health care services among vulnerable individuals combined with limited available resources requires a rethinking of the provision of oral health services. This study aimed to determine the usefulness of the dental panoramic radiograph (DPR) as an imaging tool to assess oral health and prioritize dental interventions in vulnerable patients. Methods: This observational and retrospective study evaluated charts of patients who attended Public Health Dental Clinics (PHDC), Alberta Health Services (AHS), in Calgary, Canada, between January 2018 and December 2019. Data collected included sex and age at the time of image acquisition. The following radiographic findings were gathered: the number of missing, present, decayed, restored, and impacted teeth; periapical lesions; retained root; periodontal bone loss; odontogenic and non-odontogenic lesions in the jaws; carotid calcification; and incidental radiographic findings with clinical relevance. Results: Of the 526 DPRs evaluated, 57.4% were from male patients and 42.6% were from female patients, with a mean age of 38.5 years. The average number of present teeth in females and males was 23.7 and 22.6, respectively. The most prevalent dental-related finding was periodontal bone loss (81.5%), followed by periapical lesions (59.6%) and impacted teeth (27%). Among non-dental findings, osseous lesions of the jaws were found in 10.4% of the patients, and carotid atheroma had a frequency of 3.2%. Conclusion: The DPR is a useful adjunct to the clinical exam in this specific population. It provides an opportunistic overview of their oral health and necessary support to establish priorities in oral health care in a public health setting.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".