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Early Supported Discharge (Esd) : a New Clinical Practice in Montreal For Mild To Moderate Stroke Victims. intensive and interdisciplinary Stroke Rehabilitation in The Patientu2019S Home.

2017· other· en· W6965130794 on OpenAlexaboutno aff

Bibliographic record

VenueBiblioBoard Library Catalog (Open Research Library) · 2017
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRehabilitationStroke (engine)Christian ministryIntensive careClinical PracticeHealth careOccupational therapyMEDLINEAcute care

Abstract

fetched live from OpenAlex

ESD Program is an intensive rehabilitation program to support early discharge from hospital to home for patients recovering from a milder stroke through the offer of rehabilitation similar to what is offered with in-patient intensive rehabilitation.ESDu2019s goal is to promote patient experience with access to appropriate services; optimize use of in-patient rehabilitation beds for stroke patients with severe impairments while supporting patients with milder impairments to receive their rehabilitation at home, an environment conducive to generalization.A joint pilot project by CIUSSS Centre-Sud and CIUSSS Centre-Ouest de lu2019u00eele de Montreal with finance of the Quebec Ministry of Health and Social Services, was implemented for patients on the island of MontrealThe ESD team includes: physiotherapist; occupational therapist; speech therapist; social worker; nurse; neuropsychologist and special care educator. This interdisciplinary and specialized team offers intensive functional rehabilitation at home within 48 hours after discharge from hospital, as is known to be a best practice. Eligible patients are referred by the acute care hospitals and program admissibility discussed with the ESD clinical coordinator prior to patientu2019s discharge.The program started in November 2017, so far 40 patients have benefited from ESD. Objective data will be presented in our oral presentation:

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.653
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.004
Open science0.0020.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.072
GPT teacher head0.362
Teacher spread0.290 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2017
Admission routes1
Has abstractyes

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