External validation of clinical risk prediction score for elderly treated with endovascular thrombectomy
Bibliographic record
Abstract
BACKGROUND AND AIM: The thrombectomy in the elderly prediction score (TERPS) for functional outcome after anterior circulation endovascular therapy (EVT) in patients ≥ 80 years was recently developed. The aim of this study was to assess predictors of functional outcome in the elderly and validate the prediction model. METHODS: Consecutive patients treated with EVT from the Oslo Acute Reperfusion Stroke Study were evaluated for inclusion. Clinical and radiological parameters were used to calculate the TERPS, and functional outcome were assessed at 3-month follow-up. RESULTS: Out of 1028 patients who underwent EVT for acute ischemic stroke from January 2017 to July 2022, 218 (21.2%) patients ≥ 80 years with anterior ischemic stroke were included. Fair outcome, defined as modified Rankin scale ≤ 3 (mRS), was achieved in 117 (53.7%). In bivariate analyses, male sex (p 0.035), age (p 0.025), baseline National Institute of Health Stroke Scale (NIHSS, p < 0.001), pre-stroke mRS (p 0.002) and Alberta Stroke Program Early Computed Tomography score (ASPECTS, p 0.001) were associated with fair outcome. Significant predictors for fair outcome in regression analyses were lower pre-stroke mRS, adjusted odd ratio, (aOR) 0.67 (95% CI 0.50-0.91, p 0.01), NIHSS, aOR 0.92 (95% CI 0.87-0.97, p 0.002), and higher ASPECTS, aOR 1.22 (95% CI 1.03-1.44, p 0.023). The area under the curve (AUC) using TERPS was 0.74 (95% CI 0.67-0.80). CONCLUSIONS: The risk prediction score TERPS showed moderate performance in this external validation. Other variables may still be included to improve the model and validation using other cohorts is recommended. TRIAL REGISTRATION: NCT06220981.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".