Reply to “Precision Medicine and Stroke Rehabilitation in Atrial Fibrillation: Exploring the Multidimensional Impacts of Vessel Occlusion, Age, and Treatment”
Bibliographic record
Abstract
Thank you for your interest in our paper published in the Annals of Neurology.1 We agree that post-stroke rehabilitation is a crucial yet currently neglected part of acute stroke research. Observational studies and randomized controlled trials assessing acute interventions and stroke management do often not report on the duration and intensity of rehabilitation. We agree that it would be desirable that this information be included in any study assessing long-term functional outcomes. Ideally, it should be standard just like reporting vascular comorbidities or concomitant medication. The aim of our study was to assess the prevalence of intracranial vessel occlusion in patients with atrial fibrillation presenting with acute ischemic stroke. We found that intracranial vessel occlusion detectable on imaging is common and mainly affects the anterior circulation. We feel that these findings may be of particular interest to the community because they support the evaluation of novel treatment strategies aimed at the prevention of large vessel occlusion embolic stroke.2 In addition, we found that vessel occlusion detectable on imaging was associated with poor functional outcome as assessed by the modified Rankin Scale at 90 days following stroke, despite good access to recanalization treatment such as mechanical thrombectomy. We regret that we are unable to report on rehabilitation duration and intensity because this information is not being collected in the Swiss Stroke Registry. However, Switzerland has a highly developed healthcare system with universal access to acute care and rehabilitation. All certified Stroke Units and Centres contributing to the Swiss Stroke Registry collaborate with dedicated rehabilitation facilities and have standardized post-stroke care pathways. Therefore, we are confident that all patients included in our study received high-quality rehabilitation care. Finally, we would like to thank Shu and colleagues for highlighting that anatomical variations may influence infarct size and post-stroke prognosis. We regret that this level of detail is not available in the Swiss Stroke Registry.
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 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 | 0.014 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.038 | 0.061 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".