A Semi-Centralized Retrieval Center Model
Bibliographic record
Abstract
Introduction Québec has a population of 8 million dispersed over 1,677,000 km2 (mostly concentrated in 2 metropolitan areas). Over 70 hospitals serve as deceased donor identification centers. A semi-centralized system of dedicated retrieval hospitals was established, and 9 hospitals were designated as retrieval centers (including the transplant centers). Since 2013, a pilot project of dedicated intensive care, operating room and physician resources to facilitate the functioning of the system is underway at a 1 site. This report represents an analysis of 5 years of system performance since this reorganization. Methods Data on system performance were obtained from the ODO database from the years 2012-2016. Indicators reported include: number of donors, of retrieved and transplanted organs, the average number of organs transplanted per donor, adjusted for donor age and extended criteria. Results Over a 5-year period, the annual number of organ donors increased by 43% (120 to 172). Of the 781 donors, 79.8 % (623) were retrieved in 4 high volume hospitals (≥20 donors/year), while 20.2% (158) were retrieved in 14 hospitals (≤19 donor/year). 3 hospitals achieved rates of ≥ 4 organs per donor. 1 low volume site (average of 6-9 donors/year) achieved a mean of 3.94 organs per donor. During this time, family refusal of consent decreased by 30%. An increasing number of organ retrievals are happening at low volume sites (≤5 donors/year, from 2 to 8). This semi-centralized organ retrieval model presents several advantages: the development of local expertise in donor maintenance and retrieval; support for teaching and training; commitment of the donation and transplant centers; sustained engagement of local teams (ICU, ER, OR, etc.); the development of a local culture of organ donation; and the ability to count on centers of excellence with regional/supraregional mandates. Several challenges exist: maintaining the flexibility to retrieve outside of the designated hospitals when required (e.g. DCD, family refusal of transfer); developing the culture of organ donation outside of the designated retrieval hospitals. Conclusion In the Québec system, this semi-centralized model has led to improvement in several metrics, including increased numbers of donors and numbers of organs retrieved per donor, while recognizing and supporting the contributions of lower volume centers. An additional benefit is the system-wide development of the culture 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.007 |
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".