Bibliographic record
Abstract
Database research for stroke have contributed to the development of evidence and the establishment of guidelines for stroke management. In Japan, the Fukuoka stroke registry, a regional cohort̶which provides highly accurate data̶and the Japan stroke data bank̶which includes nationwide comprehensive data̶have been collected through individual questionnaires (as revealed by a conventional epidemiological study). Recently, studies using existing data, such as insurance claims data, have become popular (as revealed by a data-driven epidemiological study). The nationwide survey of acute stroke care capacity for proper designation of comprehensive stroke center in Japan (J-ASPECT study) is assessing the quality of stroke care to close the gap between guidelines and clinical practice using data from the Diagnosis Procedure Combination, developed as a measurement tool to standardize, evaluate, and improve the quality of healthcare in Japan and to clarify the content of acute-phase hospital care. The National Database of Health Insurance Claims and Specific Health Checkups is the largest database globally that facilitates the visualization of stroke care in Japan. Large-scale real-world databases̶such as the Registry of the Canadian Stroke Network and Riks-Stroke̶have been constructed worldwide. In both cases, data are collected without obtaining consent and are linked to existing data, such as administrative data. However, the environment for database research has not been sufficiently developed from the perspective of personal information protection and research ethics in Japan. In this era of medical big data, extensive medical information is automatically recorded electronically. Medical DX (digital transformation) aims to enable the appropriate use of such medical information and linking it with existing data̶such as insurance claims data and administrative data. A large database research utilizing electronic information can change the future of stroke care.
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 | 0.046 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".