IS ACROMEGALY A HYPERCOAGULABLE CONDITION? CASE REPORTS AND REVIEW OF THE LITERATURE
Bibliographic record
Abstract
Introduction: Cardiovascular complications are a major cause of morbidity and mortality in patients with uncontrolled acromegaly. However, there are no published reports of an increased risk of venous thromboembolism (VTE) in such patients. We report three patients with uncontrolled acromegaly who presented with VTE. Clinical Cases: A 52-year-old male with uncontrolled acromegaly despite transsphenoidal (TSP) surgery and medical therapy presented in 2012 with acute chest pain and shortness of breath that was later con rmed as secondary to pulmonary embolism. A 44-year-old male immigrant, previously treated for acromegaly with radiation therapy alone, in 1992, in his native country, was referred to our centre in 2006 for acromegaly which remained uncontrolled despite medical therapy until 2009 when he achieved remission through TSP surgery. He had several episodes of VTE between 2008 and 2010. A 69-year-old male with uncontrolled acromegaly for 28 years despite two surgical resections and radiation therapy in 1986 and 1992, as well as continuous medical therapy, presented with VTE of the right axillary vein and bilateral pulmonary emboli in 2011. A thrombophilia screen in case 1 showed mild protein S de ciency, case 2 was homozygous for factor V Leiden (FVL) mutation and case 3 was heterozygous for FVL. Extensive investigations revealed no evidence of malignancy and echocardiography showed preserved ejection fraction in all three patients. Conclusion: Patients with uncontrolled acromegaly may be at increased risk of VTE. However, larger studies are required to further assess this association and determine the underlying cause. Key words: Acromegaly, pituitary tumours, thromboembolism
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".