Back

Modeling Longitudinal Perception-Based Recovery Through the Use of Standard Stroke Recovery Metrics

Akkara, Y.; Nguyen, K. V.; Lemonick, M.; Afreen, R.; Kellner, C. P.

2025-07-01 neurology
10.1101/2025.06.30.25330599 medRxiv
Show abstract

Background and PurposeStroke is a leading cause of death and disability worldwide. Among the various domains affected by stroke, perceptual deficits are particularly under-recognized despite their significant impact on a patients quality of life. To address this gap, we conducted a systematic review to evaluate longitudinal changes in perception-recovery following stroke and developed an assessment battery using standardized stroke metrics to assess perceptual function. MethodsFollowing PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, studies using standardized stroke assessment metrics in patients with either ischemic or hemorrhagic stroke were screened using PubMed, MEDLINE, and Embase. Studies that included participants with prior neurological disability or new interventional treatment (including conservative, medical, and surgical management) were excluded. ResultsA total of 120 studies with 71,179 patients were included. Neurological recovery showed a two-phase recovery pattern, with the most rapid improvements in the first year followed by slower, fluctuating changes. In contrast, tools measuring motor ability, cognition, balance, and perceived health revealed a steady recovery concentrated within the first year, with a plateau afterward. Mood improved gradually over time, and quality of life showed consistent gains. We also identified specific items within each metric that correspond to individual subdomains, based on primary validation data and existing stroke literature, to create a perception-based assessment battery. ConclusionsThese patterns suggest that perceptual, cognitive, and functional recovery after stroke may continue well beyond the early phase, highlighting how scores evolve over time. By modeling longitudinal changes, we aim to support clinicians in understanding perception-based recovery and encourage further research into its prognostic value.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.