Emergency department visits at Rambam health care campus, Israel: non-trauma related dental conditions
Bibliographic record
Abstract
OBJECTIVES: Studies of emergency department (ED) visits for non-traumatic dental conditions (NTDCs) have been carried out in the USA and Canada. In Israel, there is a shortage of such studies. In the current retrospective study, we report on the frequency and distribution of NTDCs ED visits at Rambam Health Care Campus (Rambam), in Haifa, which is an academic hospital serving more than 2.4 million residents of Northern Israel. MATERIALS AND METHODS: The data concerning ED visits at Rambam between 2010 and 2017 were obtained retrospectively from Rambam's computerized clinical and personal database of adult patients (≥18 years) visiting the ED for NTDCs. RESULTS: Overall, 1.8% of the patients who visited the Rambam ED, were identified as presenting with NTDCs. From 2010 until 2017, the number of NTDCs admissions increased by 45%, while the total ED admissions rose by 16%. The average waiting time for maxillofacial consultations for patients with NTDCs increased from 102 min in 2010 to 138 min in 2017. The busiest hours in the ED for NTDCs were during the morning shifts (47% of daily visits). CONCLUSIONS: The results of the study show that systemic and conceptual changes are needed to reduce the number of non-trauma related applications to ED.These changes can be by increasing the number of personnel or by introducing recent advances such as tele-medicine for prescreening of patients. This change calls for a greater involvement of the health policy leaders to provide alternative solutions for emergency dental care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".