Bibliographic record
Abstract
Amniotic fluid embolism (AFE) is a catastrophic, sudden-onset event that must be recognized immediately. Despite the rarity of this condition, both maternal and perinatal morbidity and mortality are significant with AFE, even in cases ideally managed. In this article, we present five key statements covering the risk factors, clinical presentation, and management of AFE in a clinical setting. The purpose of these tips is to provide clinicians with information that may improve their ability to make a timely diagnosis and establish appropriate supportive treatment to patients suffering from AFE. RésuméL’embolie amniotique est un événement catastrophique d’apparition soudaine qui doit être détecté immédiatement. Malgré la rareté de cette affection, la morbidité et la mortalité maternelles et périnatales sont importantes, même dans les cas où le traitement est idéal. Dans cet article, nous présentons cinq énoncés clés qui portent sur les facteurs de risque, le tableau clinique et la prise en charge de l’embolie amniotique dans un contexte clinique. Ces astuces visent à fournir aux cliniciens de l’information qui pourrait améliorer leur capacité à poser un diagnostic en temps opportun et à assurer un traitement de soutien approprié aux patientes atteintes d’une embolie amniotique.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".