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Record W2890723064 · doi:10.23889/ijpds.v3i4.814

Ontario’s stroke report cards: Cross-continuum data linkage allows evaluation of system of care

2018· article· en· W2890723064 on OpenAlexaffabout
Ruth Hall, Ferhana Khan, Jen Levi, Huiting Ma, Cally Martin, Cheryl Moher, Linda Kelloway, Elizabeth Linkewich, Mark Bayley

Bibliographic record

VenueInternational Journal for Population Data Science · 2018
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsToronto Rehabilitation InstituteHealth Sciences CentreSunnybrook Health Science CentreRoyal Victoria HospitalKingston Health Sciences Centre
Fundersnot available
KeywordsReport cardKnowledge translationStroke (engine)Best practiceInterdependenceLinkage (software)Balanced scorecardMedicineMedical emergencyBusinessPsychologyComputer scienceProcess managementKnowledge managementPolitical scienceEngineering

Abstract

fetched live from OpenAlex

IntroductionReport cards or scorecards typically reflect one particular sector along the care continuum; however, stroke patients typically require acute care, inpatient rehabilitation and community care highlighting the need to link data sources to demonstrate the interdependencies between and across sectors.
 Objectives and Approach1) Identify stroke best practice indicators from across the care continuum; 2) develop a one page report card that reports on the quality of the stroke system of care through data linkage and 3) visually impactful knowledge translation tool. The indicators cover five health care sectors starting with pre-hospital stroke symptom onset, then to management of the acute event, to institutional and community-based rehabilitative care, reintegration into the community and secondary prevention. The report card is a knowledge translation tool that identifies gaps in best practice, provides achievable benchmarks of regional and provincial stroke system performance to drive system change.
 ResultsUsing data linkage techniques, seven administrative datasets are used to populate the 20 indicators in the annual Ontario stroke report card. Indicator performance was trended by comparing the previous 3 years’ results to the most recent year of data. Fifteen of 17 indicators improved (11 statistically significant) compared to the previous three years and 2 indicators did not change / declined. Performance benchmarks were calculated using Achievable Benchmarks of Care™ methodology and 14 of 16 performance benchmarks improved since 2014/15. There was wide variation across indicators with only 4 indicators showing a reduction in regional variation. The Ontario stroke report card can be viewed at https://www.ices.on.ca/Publications/Atlases-and-Reports/2017/Stroke-Report-Cards.
 Conclusion/ImplicationsThe Ontario stroke report card spans the stroke care continuum, provides a snapshot of Ontario’s stroke system performance. Data linkage is essential for a system-wide opportunity to evaluate and influence system performance. This cross-continuum approach and report card format could be applied to other health related conditions.

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)0.002
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 score0.113
Threshold uncertainty score0.439

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0020.001
Research integrity0.0000.000
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 head0.106
GPT teacher head0.448
Teacher spread0.342 · 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".

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Citations0
Published2018
Admission routes2
Has abstractyes

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