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Record W2127366545 · doi:10.1212/wnl.0b013e3182a55ec7

The complexities of acute stroke decision-making

2013· article· en· W2127366545 on OpenAlexaffabout
Michel Shamy, Cheryl Jaigobin

Bibliographic record

VenueNeurology · 2013
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of CalgaryUniversity Health Network
Fundersnot available
KeywordsStroke (engine)Acute strokeMedicineClinical decision makingPhysical medicine and rehabilitationIntensive care medicineInternal medicineEngineeringTissue plasminogen activator

Abstract

fetched live from OpenAlex

OBJECTIVE: We hypothesized that low rates of tissue plasminogen activator (tPA) use are only partially explained by medical considerations, and that biases, beliefs, systems, and uncertainty affect acute stroke decision-making. METHODS: We generated a list of factors potentially influential in acute stroke decision-making: uncertainty, patient demographics that may predispose to bias (age, sex, comorbidities), physician experiences and beliefs, and systems factors. An online survey was distributed to neurologists in the province of Ontario, Canada, to assess the influence of these elements. A response rate of 69% was achieved. RESULTS: Seventy-nine percent (79%) of respondents were less likely to administer IV tPA to patients with dementia, and many were less likely to treat patients from nursing homes, with more severe strokes, or over age 80. All respondents recognized the presence of diagnostic uncertainty, and 87% believed that uncertainty in interpreting advanced imaging affected their use of tPA. The majority of respondents (70%) believed that a large left middle cerebral artery territory stroke was a fate worse than death. Four percent did not believe that IV tPA is an effective treatment for stroke. CONCLUSIONS: This study provides evidence for the presence of uncertainty, beliefs, and biases in acute stroke decision-making. This survey should be considered a preliminary investigation of the multiple factors implicit in IV tPA administration.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.014
GPT teacher head0.275
Teacher spread0.261 · 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 designNot applicable
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

Citations52
Published2013
Admission routes2
Has abstractyes

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