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Record W2048391035 · doi:10.3109/02699052.2012.750741

An examination of discharge against medical advice from brain injury inpatient rehabilitation

2013· article· en· W2048391035 on OpenAlexafffund
Il Hwan Kim, Angela Colantonio

Bibliographic record

VenueBrain Injury · 2013
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsUniversity of TorontoToronto Rehabilitation Institute
FundersCanadian Institutes of Health Research
KeywordsRehabilitationMedicineTraumatic brain injuryAgainst medical adviceMedical advicePatient dischargePhysical therapyMedical emergencyPhysical medicine and rehabilitationMEDLINEPsychologyPsychiatryPediatrics

Abstract

fetched live from OpenAlex

OBJECTIVE: Discharges against medical advice (DAMA) have been considered predictors of adverse outcomes for patients in acute care and psychiatric hospitals. However, little is known about the profile of patients who discharge AMA admitted to rehabilitation hospitals. The aims of this study were to provide a profile of patients who received inpatient rehabilitation services following a traumatic brain injury (TBI) who discharged AMA and to compare this group with the regular discharge group. RESEARCH DESIGN: Retrospective cohort study. METHODS: Hospital discharge data from two national administrative databases were reviewed for the years 2001-2006. RESULTS: The databases yielded 1559 cases of TBI (average length of stay = 51 days). Of these, 31 (2.0%) had recorded DAMA events: one in 50 patients left rehabilitation against medical advice. Compared to regular discharge (n = 1247), DAMA was significantly associated with unemployment, intentional injury, higher motor functions at admission and shorter length of stay. Known factors for DAMA in acute hospitals, such as male sex, young age and substance abuse history, were not significant. CONCLUSION: Careful screening and assessment of patients who discharge AMA could enable better prevention and management strategies, thus improving health outcomes and enhancing healthcare delivery.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.969
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.017
GPT teacher head0.363
Teacher spread0.347 · 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 teacher head, not a consensus.

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

Citations4
Published2013
Admission routes2
Has abstractyes

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