Bibliographic record
Abstract
2019年12月に中国武漢市に端を発した，新型コロナウイルス(SARS-CoV-2)感染症(COVID-19)は瞬く間に世界中に拡大し，日本でも2020年２月頃から感染者が増加した。この未知のウイルスに対して，一般の社会だけでなく，患者の診療にあたるわれわれ医療従事者も混乱に陥った。医療現場の混乱は，COVID-19患者に対してだけでなく，COVID-19患者以外の患者に対しても波及し，予定されていた手術が受けられない，定期的な診察を受けられないなどの不利益が生じた。このような医療現場の混乱は，より事態が悪化すると医療崩壊へとつながるため，政治や医療などさまざまな立場から対策がとられてきた。 本稿では，特に小児科の外来において，COVID-19患者の診療，また医療従事者の感染防御，予防接種や健診事業の維持などについて示された「小児の外来診療におけるコロナウイルス感染症2019(COVID-19)診療指針」(以降，「小児外来COVID-19診療指針」)1)の特徴と要点を解説する。実際の現場に携わる諸賢には，是非，原本を手にとって参考にしていただければ幸いである。
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.011 |
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".