Flow patterns and preferred sites of atherosclerotic lesions in the human aorta – II. Abdominal aorta
Bibliographic record
Abstract
OBJECTIVE: As in Part I, to elucidate the role of fluid mechanical factors in the localized genesis and development of atherosclerotic lesions in man, here in the abdominal aorta. METHODS: Flow patterns and preferred sites of atherosclerotic lesions in the aorta were studied in detail using the same isolated transparent aortic trees prepared from humans postmortem and the flow visualization and cinemicrographic techniques as in Part I. RESULTS: Under steady flow simulating mid-systole, the flow was found to be disturbed at the aorto-celiac and aorto-superior mesenteric artery junctions by the formation of complex secondary and adverse flows along the lateral and posterior walls of the abdominal aorta. More complex secondary and adverse flows formed at the branching sites of the left and right renal arteries. Furthermore, considerable interactions occurred between the secondary and adverse flows formed at the branching sites of the above four arteries, resulting in the formation of a large and long recirculation zone along the lateral and posterior walls of the abdominal aorta corresponding to these branches. The velocity profile was almost flattened throughout the entire length of the descending aorta. CONCLUSIONS: Atherosclerotic lesions were found mainly at the posterior and lateral walls of the abdominal aorta where slow adverse and recirculation flows formed and where wall shear stress was low.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".