Supply chain management: Stategy, planning, and operation / Sunil Chopra, Pater
Bibliographic record
Abstract
Current management of patients with aortic valve disease, including aortic valve stenosis (AS), aortic valve regurgitation (AR), and mixed aortic valve disease (MAVD), remains challenging. American and European guideline recommendations regarding the timing of intervention are mainly based on the assessment of disease severity (ie, grading), presence of symptoms related to aortic valve disease, left ventricular systolic dysfunction, or LV enlargement. Furthermore, the decision regarding the type of intervention (ie, surgical vs transcatheter) is primarily based on risk assessment from surgical risk scores. There is, however, less emphasis on the importance of the assessment of anatomic and functional cardiac repercussions of aortic valve disease to guide the clinical management of these patients. Recently, a novel approach has been proposed to improve the management of aortic valve disease with 2 main components for risk stratification of the disease: 1) grading the severity of aortic valve disease, and 2) staging the extent of extravalvular cardiac damage associated with aortic valve disease with the use of echocardiography. To date, this novel approach of extravalvular cardiac damage staging was proposed and validated only in the context of AS but could be extended to other valvular heart diseases, including AR and MAVD. Further studies are also needed to test the incremental value of additional imaging parameters (eg, myocardial fibrosis by magnetic resonance) as well as blood biomarkers (eg, natriuretic peptide, cardiac troponin, and others) to the existing cardiac damage staging schemes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
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 teacher head, 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".