Placenta Accreta Spectrum Disorders: Knowledge Gaps in Anesthesia Care
Bibliographic record
Abstract
Placenta accreta spectrum (PAS) disorder is a potentially life-threatening condition that can occur during pregnancy. PAS puts pregnant individuals at a very high risk of major blood loss, hysterectomy, and intensive care unit admission. These patients should receive care in a center with multidisciplinary experience and expertise in managing PAS disorder. Obstetric anesthesiologists play vital roles in the peripartum care of pregnant patients with suspected PAS. As well as providing high-quality anesthesia care, obstetric anesthesiologists coordinate peridelivery care, drive transfusion-related decision making, and oversee postpartum analgesia. However, there are a number of key knowledge gaps related to the anesthesia care of these patients. For example, limited data are available describing optimal anesthesia staffing models for scheduled and unscheduled delivery. Evidence and consensus are lacking on the ideal surgical location for delivery; primary mode of anesthesia for cesarean delivery; preoperative blood ordering; use of pharmacological adjuncts for hemorrhage management, such as tranexamic acid and fibrinogen concentrate; neuraxial blocks and abdominal wall blocks for postoperative analgesia; and the preferred location for postpartum care. It is also unclear how anesthesia-related decision making and interventions impact physical and mental health outcomes. High-quality international multicenter studies are needed to fill these knowledge gaps and advance the anesthesia care of patients with PAS.
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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".