Noninvasive Angiographic Results of Clipped or Coiled Intracranial Aneurysms: An Inter- and Intraobserver Reliability Study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Noninvasive angiography is commonly used to assess the outcome of surgical or endovascular treatment of intracranial aneurysms in clinical series or randomized trials. We sought to assess whether a standardized 3-grade classification system could be reliably used to compare the CTA and MRA results of both treatments. MATERIALS AND METHODS: An electronic portfolio composed of CTAs of 30 clipped and MRAs of 30 coiled aneurysms was independently evaluated by 24 raters of diverse experience and training backgrounds. Twenty raters performed a second evaluation 1 month later. Raters were asked which angiographic grade and management decision (retreatment; close or long-term follow-up) would be most appropriate for each case. Agreement was analyzed using the Krippendorff α (αK) statistic, and the relationship between angiographic grade and clinical management choice, using the Fisher exact and Cramer V tests. RESULTS: Interrater agreement was substantial (αK = 0.63; 95% CI, 0.55–0.70); results were slightly better for MRA results of coiling (αK = 0.69; 95% CI, 0.56–0.76) than for CTA results of clipping (αK = 0.58; 95% CI, 0.44–0.69). Intrarater agreement was substantial to almost perfect. Interrater agreement regarding clinical management was moderate for both clipped (αK = 0.49; 95% CI, 0.32–0.61) and coiled subgroups (αK = 0.47; 95% CI, 0.34–0.54). The choice of clinical management was strongly associated with the size of the residuum (mean Cramer V = 0.77 [SD, 0.14]), but complete occlusions (grade 1) were followed more closely after coiling than after clipping (P = .01). CONCLUSIONS: A standardized 3-grade scale was found to be a reliable and clinically meaningful tool to compare the results of clipping and coiling of aneurysms using CTA or MRA.
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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 | 0.032 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".