Pregnancy-related critical illness in ICU
Bibliographic record
Abstract
妊産婦死亡が未だ少なからず存在する。1人の妊産婦死亡あたり，9人の重症妊産婦が発生している。しかし，通常ICUで妊産婦が占める割合は約1.5％と比較的稀である。稀な症例に対応するには，頻度の高い疾患（妊娠高血圧症候群，産科出血，産科関連敗血症）を把握しておく必要がある。一般的に，集中治療を要する重症妊産婦において，これら3疾患がICU入室理由の70％近くを占める。また，妊娠に伴う生理学的変化は，多臓器にわたって予備力を小さくし，非妊娠時に比べより重篤な状態に陥りやすくする。上記3疾患と生理学的変化の関連を理解することは，病態の把握に重要となる。そして，生理学的予備力の減少は胎児をも容易に危機的状況へと曝してしまう。集中治療医が重症妊産婦および胎児の予後改善に貢献するには，重症妊産婦の疫学と特有疾患を知り，妊娠に伴う多臓器の生理学的変化を把握し，胎児危機に陥る病態を早期に認識することである。
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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".