Clinical expert consensus document on the use of percutaneous left ventricular assist devices during complex high-risk PCI in India using a standardised algorithm
Bibliographic record
Abstract
Over the past decade, percutaneous left ventricular assist devices (pLVAD), such as the Impella microaxial flow pump (Abiomed), have been increasingly used to provide haemodynamic support during complex and high-risk revascularisation procedures to reduce the risk of intraprocedural haemodynamic compromise and to facilitate complete and optimal revascularisation. A global consensus on patient selection for the use of pLVADs, however, is currently lacking. Access to these devices is different across the world, thus, individual health care environments need to create and refine patient selection paradigms to optimise the use of these devices. The Impella pLVAD has recently been introduced in India and is being used in several centres in the management of high-risk percutaneous coronary intervention (PCI) and cardiogenic shock. With this increasing utilisation, there is a need for a standardised evaluation protocol to guide Impella use that factors in the unique economic and infrastructural characteristics of India's health care system to ensure that the needs of patients are optimally managed. In this consensus document, we present an algorithm to guide Impella use in Indian patients: to establish a standardised patient selection and usage paradigm that will allow both optimal patient outcomes and ongoing data collection.
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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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".