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Record W1543652602 · doi:10.1161/str.45.suppl_1.tp281

Abstract T P281: Facilitating Best Practices in Rehabilitation for Persons With Stroke: Use of a Triage Tool in Toronto

2014· article· en· W1543652602 on OpenAlexaffabout
Shelley Sharp, Jacqueline Willems, Elizabeth Linkewich, Nicola Tahair, Charissa Levy, Mark Bayley

Bibliographic record

VenueStroke · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsToronto Rehabilitation InstituteOntario Stroke Network
Fundersnot available
KeywordsMedicineRehabilitationTriageReferralStroke (engine)Acute carePhysical therapyHealth careMedical emergencyFamily medicine

Abstract

fetched live from OpenAlex

Background: Best practice indicates all stroke patients (including severely affected) benefit from timely and intensive rehabilitation care. Currently in Toronto 27% of patients with stroke are discharged to inpatient rehabilitation from acute care (Canadian Institute for Health Information (CIHI) 11/12). Sixty percent of admissions to rehabilitation were patients with moderate stroke, 17% mild and 22% severe CIHI (FY12-13 Q1-3). Access to rehabilitation in Toronto is not equitable as admission criteria and rehabilitation programming are not standardized for stroke. Purpose: Develop a triage tool to support clinical decision making, equitable access to care and early referral to appropriate rehabilitation based on best practice Methods Acute and rehabilitation leaders collaboratively developed the triage tool. Provincial expert panel recommendations and existing referral frameworks were considered. The AlphaFIM® tool was used as the basis for categorizing stroke severity. Agreement was reached to support automatic acceptance of patients referred to rehab with AlphaFIM® score of 60-80. The tool was implemented Feb 1, 2013. All rehabilitation organizations agreed to standardized admission criteria and are working toward best practice for stroke inpatient rehabilitation care. Results: Baseline data (January to August 2012) for patients referred with an early AlphaFIM® of 60-80 indicated only 66% were accepted to rehab, 15% were declined and 8.5% had a decision pending. For those declined, 10% were identified as having special needs that could not be met, 29% were considered more appropriate for slow stream rehab, and 14% because of limited sitting tolerance and balance. An analysis of data following implementation will be presented. Conclusion: The triage tool creates a standard of best practice for the system. Agreement on common admission criteria and standard of practice for rehabilitation referral management for patients with AlphaFIM® 60-80 have been established between referrers and rehab providers. It is expected that transition barriers for this group should be minimal unless special needs are identified.

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)0.005
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.182
GPT teacher head0.489
Teacher spread0.307 · 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

Citations1
Published2014
Admission routes2
Has abstractyes

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