Solid Organ Transplantation in Patients With Preexisting Malignancies in Remission
Bibliographic record
Abstract
BACKGROUND: Solid-organ transplant recipients with pretransplant malignancies (PTM) have worse overall survival (OS) compared to recipients without history of malignancy. However, it is unknown whether the increased risk of mortality is due to recurrent cancer-related deaths. METHODS: All solid-organ transplant recipients in Ontario between 1991 and 2010 were identified and matched 1:2 to recipients without PTM using a propensity score. OS was compared using the Kaplan-Meier estimator and Cox proportional hazard models. For cancer-specific mortality and cancer recurrence, cause-specific hazard models were used and the cumulative incidence was plotted. RESULTS: Recipients with PTM had a worse OS compared with recipients without PTM (median OS, 10.3 years vs 13.4 years). Recipients with PTM were not only at increased risk of cancer-specific mortality (cause-specific hazard ratio, 1.85; 95% confidence interval [CI], 1.20-2.86) but also at increased risk of noncancer death (cause-specific hazard ratio, 1.29; 95% CI, 1.08-1.54). Compared with recipients without PTM, recipients with high-risk PTM had higher all-cause mortality (hazard ratio, 1.81; 95% CI, 1.47-2.23). Recipients with low-risk PTM were not at increased risk (hazard ratio, 1.06; 95% CI, 0.86-1.31). CONCLUSIONS: Recipients with PTM are at increased risk of all-cause mortality compared to recipients without PTM. This increased risk was noted for both cancer-specific and noncancer mortality. However, only those with high-risk PTM had worse outcomes.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".