Risk Models for Adverse Events in Microsurgery for Intracranial Unruptured Aneurysms
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Preventive treatment of unruptured intracranial aneurysms (UIAs) requires assessment of treatment risks vs expected benefit. Although established scores exist to estimate rupture and growth risk, currently, no externally validated tools exist to estimate the risks of microsurgical treatment of UIAs. Clinical prediction models based on machine learning enable generation of personalized risk estimates for each individual patient based on their specific patient and aneurysm characteristics. METHODS: Using data from 20 international centers from the prediction of adverse events after microsurgery for intracranial unruptured aneurysms study on patients treated microsurgically for UIAs, we developed and externally validated clinical prediction models for 3 outcomes measured at hospital discharge: poor neurological outcome (modified Rankin Score ≥3), new sensorimotor neurological deficits, and all-cause adverse events (Clavien-Dindo Grade ≥1). RESULTS: A total of 3705 patients were included. Data from 13 centers (2881, 78%) were used for model development. Fully trained models were evaluated on 824 patients (22%) from 7 additional centers. Average age was 56 ± 12 years, and 1049 (28%) were male. At discharge, poor neurological outcome was seen in 514 patients (14%). New sensorimotor deficits were observed in 534 patients (14%), and 894 patients (24%) experienced adverse events until discharge. At external validation, prediction of poor neurological outcome was achieved with good calibration and an area under the curve (AUC) = 0.70 (95% CI: 0.63-0.75). Similarly, new neurological deficits were predicted with good calibration and with an AUC = 0.69 (95% CI: 0.63-0.74). Prediction of all-cause adverse events only achieved an AUC = 0.59 (95% CI: 0.55-0.64) with fair calibration. The prediction model was integrated into a web application accessible at https://neurosurgery.shinyapps.io/PRAEMIUM/ . CONCLUSION: The developed models for prediction of poor neurological outcome and new sensorimotor neurological deficits at discharge exhibit good calibration and fair discrimination based on a multinational external validation, indicating that the predicted probabilities correspond well to real-world risks and may thus be clinically useful in more objectively estimating the risk of microsurgical treatment.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".