Bibliographic record
Abstract
More than half of survivors of stroke experience some degree of motor impairment, and spasticity can develop within days of the initial event. Patients with post-stroke spasticity (PSS) report a lower quality of life than those without spasticity, and they require regular long-term follow-up and monitoring within the healthcare system. This symposium supported a non-promotional discussion regarding the prevalence, burden, consequences, and need for identification of PSS. The benefits of PSS identification within 3 months of stroke were discussed by a panel of key opinion leaders, including Ted Wein, Neurologist and Assistant Professor of Neurology and Neurosurgery at McGill University, Montréal, Quebec, Canada; Ganesh Bavikatte, Consultant and Clinical Lead in rehabilitation medicine at the Walton Centre, Liverpool, and Honorary Senior Clinical Lecturer at the University of Liverpool, UK; and Sean Savitz, Professor of Neurology and Physical Medicine and Rehabilitation, Frank M. Yatsu MD Chair in Neurology, and Director of the Institute for Stroke and Cerebrovascular Diseases, University of Texas Health Science Center at Houston (UTHealth), Texas, US. These key opinion leaders explained that early prediction of PSS could be improved by increased awareness of the associated risk factors and tools, such as the Post-Stroke Checklist (PSC), the Spasticity Screening Tool, and the PSS Referral Tool. Finally, potential barriers to the early identification of PSS were presented, alongside strategies to overcome these barriers.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".