Agreement Regarding Diagnosis of Transient Ischemic Attack Fairly Low Among Stroke-Trained Neurologists
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Agreement between physicians to define the likelihood of a transient ischemic attack (TIA) remains poor. Several studies have compared neurologists with nonneurologists, and neurologists among themselves, but not between fellowship-trained stroke neurologists. We investigated the diagnostic agreement in 55 patients with suspected TIA. METHODS: The history and physical examination findings of 55 patients referred to the Stanford TIA clinic from the Stanford emergency room were blindly reviewed by 3 fellowship-trained stroke neurologists who had no knowledge of any test results or patient outcomes. Each patient's presentation was rated as to the likelihood that the presentation was consistent with TIA. We used 3 different scales (2-, 3-, and 4-point scales) to define TIA likelihood. We assessed global agreement between the raters and evaluated the biases related to individual raters and scale type. RESULTS: The agreement between fellowship-trained stroke neurologists remained poor regardless of the rating system used and the statistical test used to measure it. Difference in rating bias among all raters was significant for each scale: P=0.001, 0.012, and <0.001. In addition, for each reviewer, the rate of labeling an event an "unlikely TIA" progressively decreased with the number of points that composed the scale. CONCLUSIONS: TIA remains a highly subjective diagnosis, even among stroke subspecialists. The use of confirmatory testing beyond clinical judgment is needed to help solidify the diagnosis. Caution should be used when diagnosing an event as a possible TIA.
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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 | 0.033 | 0.162 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".