Factors Associated with Emergency Department Use for Non-traumatic Dental Problems: Scoping Review.
Bibliographic record
Abstract
OBJECTIVE: The use of hospital emergency departments (EDs) for non-traumatic dental problems places a significant strain on the health care system and resources. The objective of this scoping review was to identify factors associated with patients' use of EDs for such problems. METHODS: Embase, Medline-Ovid, Scopus and Web of Science databases were searched, and primary studies, systematic reviews and meta-analyses from Canada and the United States, published in English between 2007 and 2017 were selected for inclusion. RESULTS: Of 469 articles, 22 met our inclusion criteria: 6 were conducted in Canada and 16 in the United States. Identified factors associated with ED use for non-traumatic dental problems included patient demographics (age, gender, race/ethnicity, comorbidities, oral health status), accessibility (time of day, day of week, geographic location, access to dental practitioner), economic influences (insurance, inability to afford dental care, income) and social demographics (community language, homelessness, repeat use). CONCLUSION: The factors identified in this review can inform future research studies and program planning to address ED use for non-traumatic dental problems.
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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".