Predictors of Functional Outcome at 1 Year After Stroke: Analysis of INTERSTROKE Data from Pakistan
Bibliographic record
Abstract
Background and Objective Identification of early and long-term outcomes after stroke is important in stroke management strategies. The aim of this study was to analyse predictors of independence and functional outcome at 1 and 12 months post-stroke. Methods This was a prospective study of patients with first stroke who were enrolled between April 2013 and July 2015 from a single-centre tertiary care hospital in Pakistan. Patients were followed up at 1 and 12 months and assessed using the modified Rankin scale (mRs). Results A total of 395 patients with acute first strokes were enrolled. Stroke dependency (mRs score 3-5) was higher in our site at 1 month. At 1 month, 137 (34.6%) of the participants were independent (mRs 0-2), 54.1% ( n = 214) were dependent (mRs 3-5) and 11.1% ( n = 44) died. At 12 months, 86% ( n = 303/351) completed the follow-up. Of 303 participants, 35.3% ( n = 107) were independent (mRs 0-2), 35.6% ( n = 108) were dependent (mRs 3-5) and 29% ( n = 88) had died. Forty-eight patients (14%) were lost to follow-up. Overall mortality was 33% (132/395) in 1 year. At 12 months, no comorbidities (OR 3.26; 95% CI: 1.48-7.21) and normal level of consciousness at onset (OR 3.44; 95% CI: 1.93-6.12) were associated with greater post-stroke independence (mRs 0-2). Out of 395, 111 (28.1%) patients with no or minimal disability at 1 month (mRs 0-2), 32.4% showed worsening of disability (mRs 3-5) at 12 months. Conclusion At 1 year, 33% died and 36% were dependent. A large number (60%) of patients with minimal disability at 30 days worsened or died at 1 year.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".