Quality Metrics in Solid Organ Transplantation
Bibliographic record
Abstract
BACKGROUND: The best approach for determining whether a transplant program is delivering high-quality care is unknown. This review aims to identify and characterize quality metrics in solid organ transplantation. METHODS: Medline, Embase, and Cochrane Central Register of Controlled Trials were searched from inception until February 1, 2017. Relevant full text reports and conference abstracts that examined quality metrics in organ transplantation were included. Two reviewers independently extracted study characteristics and quality metrics from 52 full text reports and 24 abstracts. PROSPERO registration: CRD42016035353. RESULTS: Three hundred seventeen quality metrics were identified and condensed into 114 unique indicators with sufficient detail to be measured in practice; however, many lacked details on development and selection, were poorly defined, or had inconsistent definitions. The process for selecting quality indicators was described in only 5 publications and patient involvement was noted in only 1. Twenty-four reports used the indicators in clinical care, including 12 quality improvement studies. Only 14 quality metrics were assessed against patient and graft survivals. CONCLUSIONS: More than 300 quality metrics have been reported in transplantation but many lacked details on development and selection, were poorly defined, or had inconsistent definitions. Measures have focused on safety and effectiveness with very few addressing other quality domains, such as equity and patient-centeredness. Future research will need to focus on transparent and objective metric development with proper testing, evaluation, and implementation in practice. Patients will need to be involved to ensure that transplantation quality metrics measure what is important to them.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".