Improved keratinocyte carcinoma outcomes with annual dermatology assessment after solid organ transplantation: Population-based cohort study
Bibliographic record
Abstract
Solid organ transplant recipients have a high risk of keratinocyte carcinoma (non-melanoma skin cancer). Consensus-based transplant guidelines recommend annual dermatological examination but the impact on skin cancer-related outcomes is unclear. We conducted a population-based, retrospective, inception cohort study using administrative health databases in Ontario, Canada to evaluate the association between adherence to annual dermatology assessments (time-varying exposure) and keratinocyte carcinoma-related morbidity and mortality after transplantation. The primary outcome was the time to first advanced (highly morbid or fatal) keratinocyte carcinoma. Among 10 183 adults receiving their first transplant from 1994 to 2012 and followed for a median of 5.44 years, 4.9% developed an advanced keratinocyte carcinoma after transplant. Adherence to annual dermatology assessments for at least 75% of the observation time after transplant was associated with a 34% reduction in keratinocyte carcinoma-related morbidity or death compared with adherence levels below 75% (adjusted hazard ratio 0.66, 95% CI 0.48-0.92). Adherence levels were universally low (median proportion of time spent in adherence 0%, inter-quartile range 0-27%). Only 45% of transplant recipients had ever seen a dermatologist and 2.1% were fully adherent during the entire observation period. Strategies are needed to improve adherence rates in order to help decrease long-term morbidity after transplant.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".