Bibliographic record
Abstract
CONTEXT: Pregnancy-related hypertriglyceridemia is rare, but it can be life threatening in some patients with genetic susceptibility. Complications can include acute pancreatitis, hyperviscosity syndrome, and possibly preeclampsia. We present a case of successful management of recurrent gestational chylomicronemia due to compound heterozygous mutations in the LPL gene. EVIDENCE ACQUISITION: To outline advances in clinical management of this condition, we searched English language publications in PubMed, EMBASE, and ISI Web of Science (search terms: pregnancy, pregnancy complications, pregnan*, hyperlipoproteinemia, hypertriglyceridemia, chylomicrons, chylomicronemia) and reference lists of relevant published articles from 2002 to 2011. We identified eight case reports. EVIDENCE SYNTHESIS: Interventions reported in those cases are reviewed including: 1) low-fat diet; 2) nutritional supplements; 3) oral prescription medications; 4) parenteral heparin; 5) insulin infusion in the context of hyperglycemia; and 6) therapeutic plasma exchange. CONCLUSIONS: Overall, our recommendations are to monitor for pregnancy-related hypertriglyceridemia in those with prepregnancy fasting triglyceride level greater than 4 mmol/liter and to institute therapy when triglyceride level increases to more than 10 mmol/liter. Therapy should include a multidisciplinary team to address dietary fat restriction, appropriate supplements, and possible medications when needed. Admission to hospital is recommended in severe cases. We conclude that complications are preventable with appropriate and timely intervention.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".