Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".