How Patient Demographics, Imaging, and Beliefs Influence Tissue-Type Plasminogen Activator Use
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Understanding physician decision making is increasingly recognized as an important topic of study, especially in stroke care. We sought to characterize the process of acute stroke decision making among neurologists in the United States and Canada from clinical and epistemological perspectives. METHODS: Using a factorial design online survey, respondents were presented with clinical data to mimic an acute stroke encounter. The history, examination, computed tomographic (CT) scan, CT angiogram, and CT perfusion were presented in sequence, and respondents rated their diagnostic confidence and likelihood of treatment with tissue-type plasminogen activator after each element. Patient age, race, sex, and CT perfusion imaging results were randomized, whereas the rest of the clinical presentation was held constant. RESULTS: We collected 715 responses, of which 473 (66%) were complete. Diagnostic certainty and likelihood of treatment with tissue-type plasminogen activator rose incrementally as additional clinical data were provided. Diagnostic certainty and treatment likelihood were strongly influenced by the clinical history and the CT scan. Other factors such as physicians' personal beliefs or biases were not influential. Respondents' accuracy in interpreting CT angiographic and CT perfusion images was variable and generally low. CONCLUSIONS: Diagnostic certainty and likelihood of treatment with tissue-type plasminogen activator increase with additional clinical data, with the history being the most important factor for diagnostic and treatment decisions. Respondents had difficulty in interpreting the results of CT perfusion scans although they had little impact on treatment decisions. We did not identify treatment bias based on patient age, race, or sex.
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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 | 0.004 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".