STROKE REHABILITATION SERVICES IN PAKISTAN: CURRENT STATUS AND FUTURE DIRECTIONS
Bibliographic record
Abstract
Stroke is a leading cause of adult mortality and morbidity worldwide. The incidence of stroke is falling in developed countries, while on the rise in developing countries.1 Pakistan is a low middle-income country with an underdeveloped health care system whose major focus is on the management of communicable diseases.2 Epidemiological data on stroke in Pakistan is limited, based mostly on small samples reported from hospital data.3 Due to the combined efforts of the Pakistan Society of Neurology, Pakistan Stroke Society and the Faculty of Neurology at the College of Physicians and Surgeons of Pakistan (CPSP), the number of neurologists and stroke medicine physicians in the country has increased in the last decade.4 Diagnosis, acute evaluation and management of stroke has also improved. Areas of noted progress include early recognition of signs and symptoms of stroke, timely evacuation to a hospital, early neurology consult, availability of brain imaging (Computed Tomographic scan and Magnetic Resonance Imaging) and access to treatments including tissue plasminogen activator and endovascular procedures.4 At present, this is available only in major cities and hospitals. Despite such improvements in acute stroke management, functional outcomes and community reintegration for stroke patients in Pakistan is generally inadequate, due largely in part to the lack of multi-disciplinary stroke rehabilitation services. We aim to describe the current status of stroke rehabilitation services in Pakistan and discuss the main challenges and barriers towards providing multi-disciplinary stroke rehabilitation. Recommendations to overcome these challenges are also provided.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".