Validity of the Meyer Scale for Assessment of Coiled Aneurysms and Aneurysm Recurrence
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Both the Meyer and Raymond scales are commonly used to report angiographic outcomes following coil embolization of intracranial aneurysms. The objectives of this study were the following: 1) to assess the interobserver agreement of the Meyer and Raymond scales, and 2) to evaluate and compare their performance in predicting major recurrence at follow-up. MATERIALS AND METHODS: A retrospective series of 120 coiled aneurysms was included. Four investigators independently graded DSA images immediately posttreatment and at follow-up according to the Meyer and Raymond scales. On follow-up DSA, readers also evaluated recurrence outcome. Interobserver agreement was assessed via the intraclass correlation coefficient. The ability of posttreatment Meyer and Raymond scales to predict major recurrence was modeled by using logistic regression and assessed by using receiver operating characteristic analysis. RESULTS: For the Meyer scale, interobserver intraclass correlation coefficients were 0.58 (95% CI, 0.46-0.68) on posttreatment and 0.78 (95% CI, 0.72-0.83) on follow-up evaluations. For the Raymond scale, interobserver intraclass correlation coefficients were 0.50 (95% CI, 0.39-0.61) and 0.69 (95% CI, 0.62-0.76), respectively, for posttreatment and follow-up. The areas under the curve for the receiver operating characteristic analyses regarding the performance to predict major recurrence at follow-up were 0.69 (95% CI, 0.60-0.79) for the Meyer and 0.70 (95% CI, 0.61-0.78) for the Raymond scale. CONCLUSIONS: The Meyer scale appears consistent and reliable with observer agreement as high or higher than that of the Raymond scale. Performance of both scales in predicting the risk of major recurrence at follow-up is adequate, with no statistical difference between the scales.
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.
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 | 0.013 | 0.072 |
| 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".