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Record W2982120077

Trends and Predictors of Repeat Mental Health Visits to a Pediatric Emergency Department in Hamilton, Ontario.

2019· article· en· W2982120077 on OpenAlexaffabout
Tea Rosic, Laura Duncan, Li Wang, Mohamed Eltorki, Michael Boyle, Roberto B. Sassi, Kathryn Bennett, Lawna Brotherston, Paulo Pires, Olabode Akintan, Ellen L. Lipman

Bibliographic record

VenuePubMed · 2019
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsMcMaster Children's HospitalMcMaster University
Fundersnot available
KeywordsMedicineEmergency departmentMental healthDepression (economics)Odds ratioLogistic regressionOddsMedical recordAnxietyMedical diagnosisFamily medicineEmergency medicinePsychiatryInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: From 2007-2017, pediatric emergency department (ED) visits for mental health concerns increased by 66% in Canada, with repeat visits accounting for a significant proportion of all visits. Our objective was to examine patient and visit characteristics associated with repeat visits to a tertiary care pediatric ED for mental health concerns. METHOD: Data were obtained from the administrative records of McMaster Children's Hospital ED for mental health-related visits from February 2013-December 2017. Data on 9,018 ED visits made by 4,976 unique patients were included in this study. Logistic regression analysis was used to examine characteristics associated with repeat visit within six months of index presentation. RESULTS: =0.589 for males). CONCLUSIONS: We found that approximately one in five patients presenting to the ED for a mental health concern have a repeat visit within six months, consistent with previous studies. This study provides support for previously identified risk factors for repeat visits and offers information on interactions between patient sex and diagnosis.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.012
GPT teacher head0.252
Teacher spread0.240 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations14
Published2019
Admission routes2
Has abstractyes

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