Abstract TP64: Visual Perception, Cognition, and Non-Contrast CT Aspects
Bibliographic record
Abstract
Introduction: As image interpretation is a visual perception task, it is affected by context and task structure. The non-contrast CT Alberta Stroke Program Early CT Score (ASPECTS) is considered an objective radiologic measure of the extent of ischemic change in cases of acute ischemic stroke. We hypothesize that variability in ASPECTS reading is because readers are susceptible to interplay between higher-order, top-down information (clinical background or other imaging) and bottom-up sensory information, and that experts are more resistant to being swayed by these factors than novices. Methods: We tested the effect of three top-down background-information conditions (no information; clinical information with affected side; clinical information plus CT angiography (CTA)) and three environment/task conditions (daylight, core lab, time pressure) on the inter-rater reliability (IRR) of NCCT ASPECTS. Three raters (trainee, fellow, neuro-radiologist) independently scored 150 NCCT scans from acute ischemic stroke patients, with 50 allocated to each environmental task. This was repeated over three sessions, with one for each background-information condition. ASPECTS on CT perfusion Tmax thresholded scans served as ground truth. Results: IRR improved when clinical information and CTA were provided (ICC 0.55 - clinical + CTA, 0.41 - clinical, 0.10 - no information). In some situations (NIHSS <5, onset-to-imaging times >180 min), IRR was greater with clinical information but without CTA. Daylight and time pressure had greater IRR than core lab (ICC 0.59 and 0.7 vs. 0.34, respectively). In general, the trainee assigned lower ASPECTS relative to the fellow and expert. The expert showed greatest concordance with CT perfusion ASPECTS (ICC 0.69), and the trainee the least (ICC 0.47). Conclusion: The cognitive framework that we propose, which includes top-down and bottom-up constraints on visual perception, can explain variability in ASPECTS interpretation on NCCT.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".