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Record W2478767337 · doi:10.1111/ctr.12818

Incidence of melanoma in organ transplant recipients in Alberta, Canada

2016· article· en· W2478767337 on OpenAlexaffabout
Mimi Tran, Megan Sander, Pietro Ravani, P. Régine Mydlarski

Bibliographic record

VenueClinical Transplantation · 2016
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsUniversity of CalgaryUniversity of British Columbia
Fundersnot available
KeywordsMedicineIncidence (geometry)MelanomaOrgan transplantationIntensive care medicineTransplantationSurgeryCancer research

Abstract

fetched live from OpenAlex

BACKGROUND: Many studies have documented the increased risk of non-melanoma skin cancers in organ transplant recipients (OTRs). However, the incidence of melanoma is less well defined. To date, there have been no studies on the incidence of melanoma in Canadian OTRs. Herein, we determine the incidence and clinical features of melanoma in a cohort of OTRs in Southern Alberta, Canada. METHODS: We used the Southern Alberta Transplant database to identify kidney and liver transplant recipients between the years 2000 and 2012. This population was cross-referenced with the Alberta Cancer Registry for a diagnosis of melanoma. The clinical features of all cases were obtained, and the standardized incidence rate was calculated. RESULTS: We identified 993 OTR patients, representing 5955 person-years. Only one patient developed a melanoma post-transplant, and this was a nodular melanoma. The age-standardized incidence rate was 11 per 100 000 (0.6 per 5955), compared to 13.4 per 100 000 in the general Alberta population (incidence rate ratio of 1.29, with 95% confidence interval of 0.17 to 9.82). CONCLUSIONS: This is the first Canadian study to investigate the association between organ transplantation and melanoma. Our study did not identify an increased risk of developing a de novo melanoma post-transplant.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.673
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.324
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2016
Admission routes2
Has abstractyes

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