Early CT changes in patients admitted for thrombectomy
Bibliographic record
Abstract
OBJECTIVE: To systematically review the literature and assess agreement on the Alberta Stroke Program Early CT Score (ASPECTS) among clinicians involved in the management of thrombectomy candidates. METHODS: Studies assessing agreement using ASPECTS published from 2000 to 2015 were reviewed. Fifteen raters reviewed and scored the anonymized CT scans of 30 patients recruited in a local thrombectomy trial during 2 independent sessions, in order to study intrarater and interrater agreement. Agreement was measured using intraclass correlation coefficients (ICCs) and Fleiss kappa statistics for ASPECTS and dichotomized ASPECTS at various cutoff values. RESULTS: The review yielded 30 articles reporting 40 measures of agreement. Populations, methods, analyses, and results were heterogeneous (slight to excellent agreement), precluding a meta-analysis. When analyzed as a categorical variable, intrarater agreement was slight to moderate (κ = 0.042-0.469); it reached a substantial level (κ > 0.6) in 11/15 raters when the score was dichotomized (0-5 vs 6-10). The interrater ICCs varied between 0.672 and 0.811, but agreement was slight to moderate (κ = 0.129-0.315). Even in the best of cases, when ASPECTS was dichotomized as 0-5 vs 6-10, interrater agreement did not reach a substantial level (κ = 0.561), which translates into at least 5 of 15 raters not giving the same dichotomized verdict in 15% of patients. CONCLUSIONS: In patients considered for thrombectomy, there may be insufficient agreement between clinicians for ASPECTS to be reliably used as a criterion for treatment decisions.
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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 | 0.007 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".