A state‐wide study of dental comorbidities in psychiatric disorders resulting in avoidable emergency department presentations
Bibliographic record
Abstract
BACKGROUND: Attendances at emergency departments (EDs) for dental conditions are unnecessary and come at a significant cost to health services. METHODS: A population-based record-linkage analysis of a retrospective cohort over 2 years across state-based facilities in Queensland. This was to determine if people with mental illness were more likely than the general population to attend EDs for a range of non-traumatic or avoidable dental conditions. RESULTS: There were 1 381 428 individuals in the linked database, of whom 177 157 (13%) had a psychiatric history and 22 046 (1.5%) had one or more avoidable dental presentations. These were toothache (n = 9619), dental abscesses (n = 8449), caries (n = 1826), stomatitis (n = 1213) and gum disease (n = 939). After adjusting for confounders, psychiatric patients were significantly more likely to present with toothache, dental abscesses and caries but not stomatitis or gum disease. Depending on the dental outcome, other risk factors were male sex, lower income, rurality and Indigenous status. CONCLUSIONS: Given these findings, possible interventions should include an increased emphasis on assessing oral health in mental health or primary care, especially in non-metropolitan areas, as well as early dental referral. Service planning for this population should including easier navigation of dental services, availability outside normal office hours and free outreach dental clinics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".