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Record W2341509179 · doi:10.1177/1747493016632244

Performance of e-ASPECTS software in comparison to that of stroke physicians on assessing CT scans of acute ischemic stroke patients

2016· article· en· W2341509179 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Stroke · 2016
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineStroke (engine)Computed tomographyMagnetic resonance imagingNeurologyRadiologyDiffusion MRIReceiver operating characteristicIschemic strokeNuclear medicineAcute strokeInternal medicineIschemia

Abstract

fetched live from OpenAlex

BACKGROUND: The Alberta Stroke Program Early CT score (ASPECTS) is an established 10-point quantitative topographic computed tomography scan score to assess early ischemic changes. We compared the performance of the e-ASPECTS software with those of stroke physicians at different professional levels. METHODS: The baseline computed tomography scans of acute stroke patients, in whom computed tomography and diffusion-weighted imaging scans were obtained less than two hours apart, were retrospectively scored by e-ASPECTS as well as by three stroke experts and three neurology trainees blinded to any clinical information. The ground truth was defined as the ASPECTS on diffusion-weighted imaging scored by another two non-blinded independent experts on consensus basis. Sensitivity and specificity in an ASPECTS region-based and an ASPECTS score-based analysis as well as receiver-operating characteristic curves, Bland-Altman plots with mean score error, and Matthews correlation coefficients were calculated. Comparisons were made between the human scorers and e-ASPECTS with diffusion-weighted imaging being the ground truth. Two methods for clustered data were used to estimate sensitivity and specificity in the region-based analysis. RESULTS: In total, 34 patients were included and 680 (34 × 20) ASPECTS regions were scored. Mean time from onset to computed tomography was 172 ± 135 min and mean time difference between computed tomographyand magnetic resonance imaging was 41 ± 31 min. The region-based sensitivity (46.46% [CI: 30.8;62.1]) of e-ASPECTS was better than three trainees and one expert (p ≤ 0.01) and not statistically different from another two experts. Specificity (94.15% [CI: 91.7;96.6]) was lower than one expert and one trainee (p < 0.01) and not statistically different to the other four physicians. e-ASPECTS had the best Matthews correlation coefficient of 0.44 (experts: 0.38 ± 0.08 and trainees: 0.19 ± 0.05) and the lowest mean score error of 0.56 (experts: 1.44 ± 1.79 and trainees: 1.97 ± 2.12). CONCLUSION: e-ASPECTS showed a similar performance to that of stroke experts in the assessment of brain computed tomographys of acute ischemic stroke patients with the Alberta Stroke Program Early CT score method.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.017
GPT teacher head0.298
Teacher spread0.281 · 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