Distribution and current problems of acute endovascular therapy for large artery occlusion from a two-year national survey in Japan
Bibliographic record
Abstract
BACKGROUND: Endovascular treatment is recommended in clinical practice in Japan. However, its utilization and comprehensiveness are less well described. AIMS: To report endovascular treatment utilization and overall geographical coverage in Japan and to analyze regional differences in the number of endovascular treatments, specialists, and endovascular treatment-capable hospitals. METHODS: A national survey of members of the Japanese Society for NeuroEndovascular Therapy (JSNET) was conducted in 2017 and 2018. The total number of endovascular treatment cases per year was estimated, and the number of endovascular treatment cases per 100,000 people was calculated using the 2015 census. The distribution of treatment hospitals and JSNET specialists was mapped and the population coverage rate was determined. RESULTS: The total number of endovascular treatment cases in Japan increased by 34.5% from 2016 (7702) to 2017 (10,360). The number of endovascular treatment-capable hospitals in Japan increased from 597 in 2016 to 693 in 2017, with an average annual caseload of 14.9 in 2017. The number of JSNET specialists per hospital decreased from 1.81 in 2016 to 1.76 in 2017 because of the increase in endovascular treatment-capable hospitals. Only 50 (7.2%) hospitals had > 40 endovascular treatment cases annually. The majority (97.7%) of the Japanese population lives within a 60-min drive of any endovascular treatment-capable hospital. However, only 70.4% live within a 60-min drive of a high-volume center (>40 cases annually). CONCLUSIONS: Utilization of endovascular treatment in Japan is increasing; however, the number of cases per hospital remains low, as is the number of specialists per endovascular treatment-capable hospital. Increased number of specialists and centralization of endovascular treatment services may improve patient outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".