Inter- and Intrarater Agreement on the Outcome of Endovascular Treatment of Aneurysms Using MRA
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Patients treated with coiling are often followed by MR angiography. Our objective was to assess the inter- and intraobserver agreement in diagnosing aneurysm remnants and recurrences by using multimodality imaging, including TOF MRA. MATERIALS AND METHODS: A portfolio composed of 120 selected images from 56 patients was sent to 15 neuroradiologists from 10 institutions. For each case, raters were asked to classify angiographic results (3 classes) of 2 studies (32 MRA-MRA and 24 DSA-MRA pairs) and to provide a final judgment regarding the presence of a recurrence (no, minor, major). Six raters were asked to independently review the portfolio twice. A second study, restricted to 4 raters having full access to all images, was designed to validate the results of the electronic survey. RESULTS: The proportion of cases judged to have a major recurrence varied between 16.1% and 71.4% (mean, 35.0% ± 12.7%). There was moderate agreement overall (κ = 0.474 ± 0.009), increasing to nearly substantial (κ = 0.581 ± 0.014) when the judgment was dichotomized (presence or absence of a major recurrence). Agreement on cases followed-up by MRA-MRA was similarly substantial (κ = 0.601 ± 0.018). The intrarater agreement varied between fair (κ = 0.257 ± 0.093) and substantial (κ= 0.699 ± 0.084), improving with a dichotomized judgment concerning MRA-MRA comparisons. Agreement was no better when raters had access to all images. CONCLUSIONS: There is an important variability in the assessment of angiographic outcomes of endovascular treatments. Agreement on the presence of a major recurrence when comparing 2 MRA studies or the MRA with the last catheter angiographic study can be substantial.
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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 | 0.068 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".