Successful Low-Dose Thrombolysis of Submassive Pulmonary Embolus in a Pregnant Patient: A Case Report
Bibliographic record
Abstract
THIS ARTICLE HAS BEEN RETRACTED (Pulmonary embolus is an important cause of morbidity and mortality during pregnancy with a high case fatality rate. Due to presumed risk of maternal bleeding and fetal complications, thrombolytic therapy is relatively contraindicated during pregnancy. Thrombolysis for pulmonary embolus in pregnancy has only been used in haemodynamic compromise with shock. A 37-year-old patient presented at 34 weeks gestation with progressive breathlessness, with symptoms at rest. She had clinical signs of deep vein thrombosis and was tachycardic, tachypnoeic but had a normal systemic arterial blood pressure. Pulmonary embolus was confirmed and transthoracic echocardiography demonstrated right ventricular dysfunction and right ventricular pressure overload. Despite optimal anticoagulation therapy, the patient deteriorated clinically but remained normotensive. We administered thrombolytic therapy with 50% of the conventional dose of recombinant tissue-type plasminogen activator (Alteplase). The clinical and haemodynamic response to treatment was excellent, with no maternal or fetal complications at the time or in subsequent days to discharge.) doi: http://dx.doi.org/10.4021/cr284w
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".