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Record W2810000358 · doi:10.1177/0840470418774807

Establishing an integrated model of subacute care for the frail elderly

2018· article· en· W2810000358 on OpenAlexaffabout
Mary Boutette, Akos Hoffer, Jennifer Plant, Benoît Robert, Danielle Sinden

Bibliographic record

VenueHealthcare Management Forum · 2018
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsVeterans Affairs Canada
Fundersnot available
KeywordsDeconditioningMedicineExacerbationHealth carePopulationGerontologyPhysical therapyEnvironmental health

Abstract

fetched live from OpenAlex

The current health system in Ontario is not designed to meet the needs of frail older adults. This is particularly true for older adults hospitalized due to exacerbation of chronic illness or medical crisis. This article describes the Subacute Care Unit for the Frail Elderly (SAFE) program, one which is designed to serve frail older patients who are at risk of deconditioning or disability associated with prolonged hospitalization but who may safely return home or to a retirement home following up to 4 weeks of subacute care in a restorative environment. The program centres on an intense restorative and integrated care delivery model. The patient population is medically complex, requiring medical supervision and regular adjustment to the care plan to optimize medical status. Individuals are no longer acutely ill and are considered stable or stabilizing. Care and services are designed to improve outcomes for hospitalized frail older adults by proactively addressing the conditions that contribute to alternate level of care before the deconditioning associated with prolonged hospitalization is experienced.

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)0.005
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.389

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0050.004
Open science0.0030.011
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.002

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 head0.040
GPT teacher head0.328
Teacher spread0.288 · 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

Citations9
Published2018
Admission routes2
Has abstractyes

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