Early CT Changes in Patients Admitted for Thrombectomy: Intra- and Interrater Agreement and Systematic Review of the Literature (I6.009)
Bibliographic record
Abstract
OBJECTIVE: We aimed to systematically review the literature and assess agreement on ASPECTS amongst clinicians involved in the acute management of thrombectomy candidates. BACKGROUND: The Alberta Stroke Program Early CT score (ASPECTS) is widely used in clinical practice. DESIGN/METHODS: Studies assessing agreement using ASPECTS published in 2000-2015 were reviewed. Fifteen raters reviewed and scored the anonymized CT scans of 30 patients screened in local thrombectomy trial during two independent sessions in order to study intra- and interrater agreement. Agreement was measured using intraclass correlation coefficients (ICCs) and Fleiss’ kappa statistics for ASPECTS and dichotomized ASPECTS at various cut-off values. RESULTS: The review yielded 30 articles reporting 40 measures of agreement. Populations, methods, analyses, and results (slight to excellent agreement) were heterogeneous, precluding a meta-analysis. Agreement between clinicians on the ASPECTS of 30 patients was problematic. Intrarater ICCs varied between 0.599 and 0.943. When analyzed as a categorical variable, intrarater agreement was slight to moderate (k = 0.042 - 0.469); it reached a substantial level (k > 0.6) in 11/15 raters when the score was dichotomized (≥ 6). The interrater ICCs varied between 0.672 and 0.811, but agreement was slight to moderate (k = 0.129 - 0.315). Interrater agreement did not reach a substantial level (k = 0.593) even when ASPECTS was dichotomized (≥ 6). 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 | 0.125 | 0.352 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.025 | 0.016 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".