Management of Post-transplant Infections in Collaborating Hospitals (MATCH) Programme: a prospective cohort of all transplant recipients at Copenhagen University Hospital—Rigshospitalet, Denmark
Bibliographic record
Abstract
PURPOSE: The Management of Post-transplant Infections in Collaborating Hospitals (MATCH) programme, initiated in 2011 and still ongoing, was created to 1) optimise the implementation of existing preventive strategies against viral infections in solid organ transplant (SOT) recipients and allogenic haematopoietic stem-cell transplant (HSCT) recipients and 2) advance research in the field of transplantation by collecting data from a multitude of sources. PARTICIPANTS: All SOT and HSCT recipients at Copenhagen University Hospital, Rigshospitalet, are followed in MATCH. By February 2021, a total of 1192 HSCT recipients and 2039 SOT recipients have been included. Participants are followed life long. An automated electronic data capture system retrieves prospective data from nationwide registries. Data from the years prior to transplantation are also collected. FINDINGS TO DATE: Data entries before and after transplantation include the following: biochemistry: 13 995 222 and 26 127 817; microbiology, cultures: 242 023 and 410 558; other microbiological analyses: 265 007 and 566 402; and pathology: 170 884 and 200 394. There are genomic data on 2431 transplant recipients, whole blood biobank samples from 1003 transplant recipients and faeces biobank samples from 207 HSCT recipients. Clinical data collected in MATCH have contributed to 50 scientific papers published in peer-reviewed journals and have demonstrated success in reducing cytomegalovirus disease in SOT recipients. The programme has established international collaborations with the Swiss Transplant Cohort Study and the lung transplant cohort at Toronto General Hospital. FUTURE PLANS: Enrolment into MATCH is ongoing with no planned end date for enrolment or follow-up. MATCH will continue to provide high-quality data on transplant recipients and expand and strengthen international collaborations.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".