P126/295 Transformative progress: the development of neuro-intervention in pakistan and its trailblazers
Bibliographic record
Abstract
Introduction International healthcare professionals, neuro-interventional organisation, mentoring programmes, and social media platforms have all contributed to the development of neuro-intervention in Pakistan. Aim of Study International healthcare professionals are instrumental in developing neuro intervention as a specialty in Pakistan. Methods College of Physicians and Surgeons Pakistan (CPSP), leading government healthcare centre responsible for medical development in Pakistan, does not have a fellowship programme for neuro-intervention. Pakistani foreign healthcare specialists and neuro-interventional societies have played a critical role in developing foundational initiatives. MENA-SINO Society has created a neuroendovascular diploma for LMICs, workshops and online webinars to assist local physicians in gaining the skills required for complex neuro-interventional procedures. ESMINT has introduced the EXMINT Stroke diploma, with seven Pakistani participants this year. Social media has become an important tool for neuro-intervention in Pakistan, with platforms such as WhatsApp, Facebook, and Twitter playing a major role. Results International healthcare specialists have pledged to support the expansion of neuro-intervention in Pakistan, offering mentorship, remote consulting, and support. Examples include Prof Adnan Siddiqui of Toshiba Health Care Centre, Prof Ashfaq Shuaib of Alberta Stroke School, and Prof Adnan Qureshi of Missouri University. This has enabled Pakistani interventionists to participate in direct observership programmes in the US. Collaboration projects with foreign societies, such as MT2020 – Mission Thrombectomy, have been critical in boosting neuro-intervention in Pakistan, leading to the construction of stroke facilities and the propagation of MT awareness. Conclusion International healthcare specialists have improved patient outcomes and established Pakistan as a regional hub for neuro-intervention. Disclosure of Interest No Conflict of Interest
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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".