The optimal shape of an aortic heart valve replacement – on the road to the consensus
Bibliographic record
Abstract
The steady increase in the number of patients with diseased aortic valves demands the development of effective aortic valve replacement procedures. Engineering and technology offer various manufactured alternatives, but none can exactly match the natural human valve. In addition to the experts of heart valve tissue engineering, many researchers focus on specific aspects of the manufacturing of artificial valves. The aim of this study was to benefit such manufacturing processes. From the contributor's perspective, it is vital to gain comprehensive knowledge before embarking on this project. The perfect/optimal shape of the valve is the fundamental aspect that needs to be considered by all participants. It is noteworthy that the geometry not only limits the functionality of the structure but also determines the choice of material and engineering methods. In this study, we attempt to determine if current knowledge is sufficient to reach consensus on the issue of the optimum shape of the valve. Here, we not only provide a brief overview of traditional literature but also include the opinions of experts. This innovative way of scientific communication is unprecedented in scientific literature, and we hope that both professionals and contributors will find this study useful.
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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".