Destination left ventricular assist devices in island states: asking too much or the inevitable solution
Bibliographic record
Abstract
Abstract Background Destination left ventricular assist device placement is increasing as a result of donor shortages and changing patient attitudes. As organ shortages become critical, LVAD programs become fundamental even in more remote regions of the world including island states. Here, we provide a look into the current state and availability of LVAD programs in island states. Main body A narrative review was performed using the World Health Organization Global Index Medicus and PubMed/MEDLINE databases to identify articles describing the island states having reported LVAD placements and programs. Additionally, INTERMACS reports were used. Data were retrieved and a review is presented describing the current state of LVADs in island states. The Caribbean region as a whole has a heart failure (HF) prevalence of 814 per 100,000 and Oceania 667 per 100,000 people. We estimate that over 3000 people in these islands need either a heart transplant or an LVAD. Short conclusion For HF patients living in island regions, special attention should be paid to the inability of having access to specialized mainland medical care. The continuous quest for a solution to HF in island regions should include the establishing of high-quality LVAD programs in a transfer-network centralized/regionalized system to care for those patients not candidates for long-distance air-bridging.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".