Bibliographic record
Abstract
(Can J Anaesth. 2016;63(9):1075–1097) Failure of timely diagnosis and treatment of hypertensive disorders of pregnancy (HDP) contributes significantly to maternal and neonatal morbidities such as neurological complications, pulmonary edema, postpartum hemorrhage; neonatal respiratory and neurological complications and stillbirth. HDP affects 3% to 8% of all pregnancies and special attention to its diagnosis and treatment is needed to curb maternal and neonatal mortality and morbidity. Hypertension without proteinuria, as well as preexisting hypertension, has been occurring more frequently, specifically in older pregnant women. In Canada, the maternal and infant mortality rates have been controlled with improved medical care to women. The Society of Obstetricians and Gynaecologists of Canada (SOGC) guidelines on the classification, clinical features, laboratory investigations, diagnosis, and care of HDP, which are outlined in this article and emphasize a multidepartmental team including anesthesiologists, enhance the quality of medical care. The guidelines were published in 2008 and revised in 2014.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".