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The 6‐ and 12‐Month Outcomes of Older Medical Inpatients Who Recover from Subsyndromal Delirium

2008· article· en· W2112871670 on OpenAlexaff
Martín G. Cole, Jane McCusker, Antonio Ciampi, Éric Belzile

Bibliographic record

VenueJournal of the American Geriatrics Society · 2008
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsMcGill UniversitySt Mary's Hospital Centre
Fundersnot available
KeywordsMedicineDeliriumCohortInstitutionalisationEmergency medicinePrimary careInternal medicinePediatricsIntensive care medicineFamily medicinePsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVES: To compare the 6- and 12-month outcomes of patients who recovered from subsyndromal delirium (SSD) by 8 weeks with the outcomes of patients who did not recover or did not have an index episode. DESIGN: Secondary analysis of data collected for a cohort study of the prognosis of delirium. SETTING: University-affiliated primary acute care hospital. PARTICIPANTS: Older medical inpatients with prevalent, incident, or no SSD were classified into three mutually exclusive groups at 8 weeks (SSD-recovered, SSD-not recovered, no SSD) and followed up at 6 and 12 months. MEASUREMENTS: The primary hierarchical composite outcome was death, institutionalization, or cognitive or functional decline at 6 and 12 months. In secondary analyses, components of the primary outcome were examined separately. RESULTS: Of the 129 patients assessed at 8 weeks, 51, 47, and 31 met criteria for SSD-recovered, SSD-not recovered and no SSD, respectively. At 6 and 12 months, the primary and secondary outcomes of the SSD-recovered group were better than the outcomes of the SSD-not recovered group and, for the most part, intermediate between the outcomes of the SSD-not recovered and no SSD groups. CONCLUSION: Recovery from SSD appears to predict better longer-term outcomes than no recovery. Efforts to identify and treat SSD in older medical inpatients may improve outcomes.

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)0.000
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation 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 score0.101
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.255
Teacher spread0.245 · 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.

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

Citations45
Published2008
Admission routes1
Has abstractyes

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