CARDIAC TRANSPLANT -A SINGLE CENTRE RETROSPECTIVE OBSERVATION
Bibliographic record
Abstract
Background and Aim: Advances in pharmacological and nonpharmacological management of heart failure shifted the paradigm to transplantation of heart. Currently so many centers are doing heart transplant as the availability of donors and recipients are increasing day by day. The goal of this study is to share our experience in all our heart transplantation procedures. Ours is a tertiary care government multi super Speciality hospital. In our institute we have been doing cardiac surgeries for six years and heart transplants for past three years. In this discussion we share our experience about how we did all the procedures in our center . Method: After getting approval from institutional research committee we analyzed 8 transplants done in our center. The preoperative optimization, monitoring tools, anesthetic technique and post-operative complications and management are discussed . Apart from routine monitors we have used BIS, Cerebral oximetry and cardiac output monitors. Result: Of the eight cases, six are doing well including a (pediatric) 10-year-old recipient. Of the remaining two, one patient died on 3rd Post-Operative Day due to acute kidney injury and the other was death due to acute rejection. Conclusion: The key points we have learnt from our experience are careful selection and preparation of the donor, adequate preload with optimal inotropic support during weaning, minimizing increase in pulmonary vascular resistance and good pain relief are key aspects for successful outcome.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".