A report of the Japan ? Canada Joint Conference on Emergency Triage and Acuity System
Bibliographic record
Abstract
救急患者増加と医療現場の疲弊に対する救急医療システムとしての対応策の一つとして救急外来における救急トリアージすなわち患者緊急度判定システムの導入が必要とされている。日本での救急外来トリアージ導入に対する方向性の確認と科学的評価の確立を目的としてカナダアルバータ州エドモントンにおいて日本・カナダ合同緊急度判定支援システム研究学会を開催した。両国より医師，看護師といった医療従事者のみでなく行政の関係者からの参加も得て，救急外来患者トリアージの臨床応用にさいしての普及方策と課題および導入後の成果の評価の重要性について討議された。
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".