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Interobserver Variation of ASPECTS in Real Time

2004· article· en· W2011331088 on OpenAlexaffabout
Shelagh B. Coutts, Andrew M. Demchuk, Philip A. Barber, William Hu, Jessica Simon, Alastair M. Buchan, Michael D. Hill

Bibliographic record

VenueStroke · 2004
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsFoothills Medical Centre
Fundersnot available
KeywordsMedicineCohen's kappaKappaStroke (engine)Acute strokeRating scaleComputed tomographyIschemic strokeRadiologyNuclear medicineCardiologyIschemiaInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The Alberta Stroke Program Early CT Score (ASPECTS) has been used to quantify early ischemic changes on computed tomography (CT) brain scans of acute stroke patients. We sought to assess the reliability of the score when performed in real time as compared with an expert rating performed at a later time point. METHODS: Two hundred fourteen patients presenting with acute ischemic stroke or transient ischemic attack were prospectively recruited if they had a brain CT scan performed within 12 hours of symptom onset. Each scan was read for ASPECTS prospectively by the treating physician and later by 1 expert reader. A weighted kappa statistic was used to determine the interobserver agreement. RESULTS: The median baseline National Institutes of Health Stroke Scale score was 5 (range: 0 to 32) and the median time to CT scan was 152 minutes (range: 22 to 769). The interobserver agreement between ASPECTS performed in real time and expert ASPECTS was substantial (kappa(w)=0.69). The mean difference between real-time ASPECTS and expert ASPECTS was 0 (SD: 1.1). CONCLUSIONS: ASPECTS is a reliable clinical scale for rating early ischemic changes on CT when performed in real time.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
Threshold uncertainty score0.253

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.253
Teacher spread0.240 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations100
Published2004
Admission routes2
Has abstractyes

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