IER-START nomogram for prediction of three-month unfavorable outcome after thrombectomy for stroke
Bibliographic record
Abstract
BACKGROUND: The applicability of the current models for predicting functional outcome after thrombectomy in strokes with large vessel occlusion (LVO) is affected by a moderate predictive performance. AIMS: We aimed to develop and validate a nomogram with pre- and post-treatment factors for prediction of the probability of unfavorable outcome in patients with anterior and posterior LVO who received bridging therapy or direct thrombectomy <6 h of stroke onset. METHODS: We conducted a cohort study on patients data collected prospectively in the Italian Endovascular Registry (IER). Unfavorable outcome was defined as three-month modified Rankin Scale (mRS) score 3-6. Six predictors, including NIH Stroke Scale (NIHSS) score, age, pre-stroke mRS score, bridging therapy or direct thrombectomy, grade of recanalization according to the thrombolysis in cerebral ischemia (TICI) grading system, and onset-to-end procedure time were identified a priori by three stroke experts. To generate the IER-START, the pre-established predictors were entered into a logistic regression model. The discriminative performance of the model was assessed by using the area under the receiver operating characteristic curve (AUC-ROC). RESULTS: = 583) sets. The AUC-ROC of IER-START was 0.838 (95% confidence interval [CI]): 0.816-0.869) in the training set, and 0.820 (95% CI: 0.786-0.854) in the test set. CONCLUSIONS: The IER-START nomogram is the first prognostic model developed and validated in the largest population of stroke patients currently candidates to thrombectomy which reliably calculates the probability of three-month unfavorable outcome.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".