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Record W3026045774 · doi:10.1186/s13584-020-00385-2

Emergency department visits at Rambam health care campus, Israel: non-trauma related dental conditions

2020· article· en· W3026045774 on OpenAlexaboutno aff
Leon Bilder, Jacob Horwitz, Hadar Zigdon‐Giladi, Zvi Gutmacher

Bibliographic record

VenueIsrael Journal of Health Policy Research · 2020
Typearticle
Languageen
FieldDentistry
TopicDental Research and COVID-19
Canadian institutionsnot available
Fundersnot available
KeywordsEmergency departmentMedicineEconomic shortageHealth administrationHealth services researchMedical emergencyHealth careEmergency medicinePublic healthHealth informaticsFamily medicineNursing

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.403
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.003

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.

Opus teacher head0.107
GPT teacher head0.523
Teacher spread0.417 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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Same venueIsrael Journal of Health Policy ResearchSame topicDental Research and COVID-19French-language works237,207