Safety of splenectomy during pregnancy
Bibliographic record
Abstract
OBJECTIVE: The aim of our study is to evaluate the risk of morbidity and mortality of splenectomy in pregnant women compared with non-pregnant women. MATERIALS AND METHODS: We conducted a retrospective population-based matched cohort study using the Health Care Cost and Utilization Project, Nationwide Inpatient Sample database from 2003 to 2011. Pregnant women with splenectomy were age-matched to non-pregnant women with splenectomy. We compared risks of morbidity and mortality between pregnant and non-pregnant women using conditional logistic regression analysis. RESULTS: The non-pregnant group had an excess of white patients and a greater proportion of Medicaid and private insurance users. There was a tendency for greater frequency of laparotomies in pregnant patients. Risk of VTE, portal vein thrombosis, renal failure and sepsis were comparable between the groups. Risk for transfusion was higher amongst pregnant women (OR 2.2, 95% CI (1.7-2.8)), as was the risk for a longer hospital stay (OR 1.7, 95% CI (1.4-2.1)). CONCLUSION: Caution should be taken when performing splenectomy during pregnancy as risk for complications and mortality may be increased. Additional measures should be undertaken to have blood units on reserve for this population.
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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".