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Quicr-Alberta Stroke Program : Targeting inpatient Stroke Outcomes

2017· other· en· W6889832847 on OpenAlexaboutno aff

Bibliographic record

VenueBiblioBoard Library Catalog (Open Research Library) · 2017
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsStroke (engine)Acute strokePsychological interventionCohortPopulationCohort studyEmergency department

Abstract

fetched live from OpenAlex

Background:Reperfusion strategies in acute stroke over the past decade have dramatically altered functional outcomes in stroke patients. Strategies such as QuICR have focused on increasing the number of patients that present within the time window, thereby increasing the total number of stroke patients receiving acute stroke interventions (ASI). However, the population of patients who present with acute In Hospital Strokes (IHS), have been largely excluded from educational strategies targeting outcome. Review of the outcomes of IHS, relative to u201cout of hospital strokesu201d (OHS), indicate common trends of fewer proportions treated with ASI and poorer outcomes. IHS patients who do undergo ASI have demonstrated comparably good outcomes. A related cohort is that of patients with relapsing symptoms occurring in the emergency room (IHS-Er) setting. Methods: These groups were excluded from the first phase of the ongoing QuICR initiative of the Alberta Stroke Program. A three component strategy of: 1. Data Collection; 2. Knowledge Attitude and Practices Survey (KAPS) and 3. Education targeting this population of patients was implemented in phase 2. Results: Post implementation; an increase in the number of stroke codes called and proportion of acute strokes treated that were IHS was seen ( p = 0.02 for 2017 vs 2014). Conclusion: Concerted provincial strategies can improve access of IHS to ASI at representative Comprehensive and Primary Stroke Centres.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Bibliometrics, Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.073
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0370.013
Science and technology studies0.0020.003
Scholarly communication0.0210.028
Open science0.0260.032
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0420.038

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 head0.077
GPT teacher head0.379
Teacher spread0.301 · 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; both teacher heads agree on what is shown here.

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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