eStroke: How to Align Stakeholders and Reach Sustainability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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?
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it