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Record W2892101159 · doi:10.1161/str.49.suppl_1.tp64

Abstract TP64: Visual Perception, Cognition, and Non-Contrast CT Aspects

2018· article· en· W2892101159 on OpenAlexaffabout
Alexis Wilson, Sadanand Dey, James Evans, Carol Huilian Tham, Zarina Assis, Ericka Teleg, Pooneh Pordeli, Prasanna Venkatesan Eswaradass, Stephen van Gaal, Mohamed Najm, Anneliese Neweduk, MacKenzie Horn, Hulin Kuang, Moiz Hafeez, Wu Qiu, Andrew M. Demchuk, Mayank Goyal, Michael D. Hill, Bijoy K. Menon

Bibliographic record

VenueStroke · 2018
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineContext (archaeology)Contrast (vision)Stroke (engine)CognitionAngiographyRadiologyArtificial intelligencePsychiatry

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.010
GPT teacher head0.274
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2018
Admission routes2
Has abstractyes

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