People would rather see a physician than a dentist when experiencing a long-standing oral ulceration. A population-based study in Spain
Bibliographic record
Abstract
BACKGROUND: Primary care physicians have been reported to be the first choice for patients with oral ulcerations. This study investigates the health-seeking behaviour of lay public in Galicia (North-western Spain) if experiencing a long-standing oral ulceration. MATERIAL AND METHODS: Cross-sectional population-based survey of randomly selected respondents conducted from March 1, 2015 to 30 June 2016. RESULTS: A total of 5,727 pedestrians entered the study (response rate: 53%), mostly in the 45-64 age group (30.2%; n=1,728), 47.7% of them (n=2,729) were males. Most participants (42.1%; n=2,411) reported to visit their dentist once a year and had secondary or compulsory education as their highest educational achievement (28.18%, n=1,614; 28%, n=1,600 respectively). When questioned what they would do if they had a wound/ulceration lasting longer than 3 weeks, most participants answered they would go to see their primary care physician (62.8%; n=3,597) and less than one quarter of the sample (23.8%; n=1,371) would seek consultation with their dentist. CONCLUSIONS: General Galician population would seek professional consultation about a long-standing oral ulceration, relying mostly on primary care physicians. Those neglecting these lesions are elderly, less-schooled people and unaware of oral cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".