Bibliographic record
Abstract
An aortic aneurysm is a dilation of the wall of the aorta, the body's largest artery. This condition can be life-threatening, particularly when the aneurysm ruptures, which can lead to severe internal bleeding.Currently, the diameter of the aneurysm is considered the primary criterion in both diagnosis and the decision-making process for surgical intervention. In this study, tissue samples from over 50 patients were examined. The findings revealed that not every patient with an aneurysm has a thin aortic wall that is prone to rupture. A significant group of patients develops a thickened wall as a compensatory mechanism in response to elastin degradation. In contrast, another group lacks this compensatory response, resulting in a progressively thinning aortic wall as the dilation increases.This latter group represents a high-risk population for developing a wall rupture, which may lead to fatal bleeding. The results of this thesis demonstrate that, in addition to diameter, there are other measurable variables — such as intima-media thickness, structural abnormalities of the extracellular matrix, and cell density — which may serve as better predictors of rupture risk. Surgeons could potentially apply these parameters in clinical practice, provided that appropriate radiological techniques are developed to visualize them reliably. In this way, unnecessary surgeries can be avoided, and only patients with a high risk will undergo surgical treatment.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.010 |
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".