eStroke: How to Align Stakeholders and Reach Sustainability
Bibliographic record
Abstract
There is a massive need for stroke treatment and rehabilitation in China. In 2018, Neusoft Medical cooperated with the State Engineering Laboratory of Internet Medical Diagnosis and Treatment Technology headed by Xuanwu Hospital to create the eStroke National Thrombolysis and Thrombectomy Image Platform (eStroke, in short). The primary objective of eStroke is to shorten the time of diagnosis for proper treatment in order to improve patient survival and reduce sequelae when the patient survives. After three years, the project is well underway but needs to scale up, as only 83 hospitals have joined, and only 13,000 patients have been served. No partner is satisfied. The project was set up as a public welfare project with an agreement not to charge users. Neusoft had hoped that eStroke's user base would grow and indirectly drive equipment sales such as CT and MRI machines. However, since eStroke does not directly generate profits, sales staff had no incentive to promote eStroke. Dr. Huang Feng, who is in charge of the eStroke project at Neusoft, plans to apply for a special marketing budget from Neusoft Medical in the annual budget review meeting to expand the scale of eStroke users rapidly. Still, the concerns and demands of various stakeholders of the eStroke platform are far more complicated than simply calling for investing more capital and increasing the workforce. How should Dr. Huang consider the claims of all stakeholders? How can he persuade the company to invest more? Will the new budget alone help eStroke expand quickly?
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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 | 0.045 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.024 | 0.038 |
| Open science | 0.004 | 0.038 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.032 | 0.010 |
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".