Progress in Organ Donation and Transplantation: A Critical Review of Literature
Bibliographic record
Abstract
The majority of countries are battling with a high incidence of organ failure such as the kidneys, heart, lungs, pancreas, and liver. The only solution that can remedy the plight of patients facing the strong likelihood of death as a result of malfunctioning body organs is organ donation and transplantation. The intention of this literature study is to assess progress in organ donation and transplantation. This study has benefitted immeasurably from previous scientific investigations. Four hundred and thirty-one published papers were selected from different accredited journals. The study found that many of the countries that have implemented the opt-in system are struggling to close the gap between the high demand for and the actual availability of life-saving organs due to low rates of registered and committed organ donors. The majority of patients that are contending with end-stage diseases are added to the organ donation waiting lists, but have little hope of receiving life-prolonging organs. Among the factors that deter people from contributing to organ donation and transplantation are a lack of knowledge, the failure to obtain consent from family members or next-of-kin, social attitudes, socio-cultural aspects, and myths. This study recommends urgent measures that could be taken to increase organ transplants in public and private hospitals due to the chronic shortage of organs for transplantation and by introducing the opt-out system of organ donation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".