ArticlemedRxiv : the preprint server for health sciences2026
Acute-Phase Machine Learning Prediction of 12-Month Aphasia and Discourse Recovery.
Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Approximately 30-40% of stroke patients retain aphasia at 12 months. Early forecasting may guide rehabilitation and prognostic enrichment of clinical trials, yet machine learning (ML) prediction of language recovery has typically relied on chronic-phase data unavailable at the acute decision point. Whether acute features predict 12-month outcomes, and whether global severity and connected-speech recovery share substrates in an ML framework, is untested. We studied 73 patients with acute left-hemisphere ischemic stroke and aphasia (mean 2.8 days post-onset). Two 12-month outcomes were defined: aphasia resolution (Western Aphasia Battery-Revised Aphasia Quotient [WAB-AQ] ≥93.8) and discourse normalization (Modern Cookie Theft content units ≥22.1; N=61). Four ML algorithms were trained on four hierarchical feature sets (clinical, volumetric, anatomical, network-disconnection) using nested cross-validation and SHapley Additive exPlanations (SHAP) stability analysis. Acute WAB-AQ dominated (mean ∣SHAP∣ = 13.60, ~20× the next feature). For aphasia resolution, random forest achieved F1 = 0.874 (95% CI, 0.800-0.941), Pearson r = 0.827, mean absolute error (MAE) = 7.26 WAB-AQ points; clinical features alone achieved F1 = 0.851. For discourse, support vector regression achieved F1 = 0.725 (95% CI 0.593-0.831), r = 0.617, MAE = 8.96 content units. Three predictors were shared (acute WAB-AQ, lesion volume, left pars triangularis); ventral-stream tracts were linked to aphasia resolution, whereas interhemispheric and prefrontal connectivity were linked to discourse. Both models overpredicted severe chronic outcomes. Acute-phase ML forecasts 12-month aphasia resolution accurately and discourse more modestly. Clinical features carry most predictive variance; acute imaging reveals shared and outcome-specific substrates mapping onto dual-stream architecture, supporting early stratification for rehabilitation and prognostic trial enrichment.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.