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Record W2410284053

Acute care hospital days and mental diagnoses.

2012· article· en· W2410284053 on OpenAlexaffabout
Helen Johansen, Philippe Finès

Bibliographic record

VenuePubMed · 2012
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsMedical diagnosisMedicineAcute careMental healthQuarter (Canadian coin)Psychiatric diagnosisAltered Mental StatusEmergency medicineMental illnessHospital carePsychiatryPediatricsHealth careSchizophrenia (object-oriented programming)
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Data from the Discharge Abstract Database of the Canadian Institute for Health Information were used to examine acute care hospital days for patients with a mental condition coded as the most responsible diagnosis or a comorbid diagnosis. In 2009/2010, patients with a mental diagnosis represented 11.8% of people who had been hospitalized and 25.5% of acute care hospital days. Those for whom the mental condition was the most responsible diagnosis accounted for 9.0% of hospital days (1.2 million), and those with a comorbid mental diagnosis accounted for 16.5% of hospital days (2.3 million). Mental diagnoses were often associated with physical conditions. The average hospitalization with a mental diagnosis was two and a half times as long as the average for hospitalizations without a mental diagnosis. About one-quarter of hospital days with a mental diagnosis were designated as alternate level of care days.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.515
Threshold uncertainty score0.976

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.011
GPT teacher head0.244
Teacher spread0.233 · 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

Citations12
Published2012
Admission routes2
Has abstractyes

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