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Record W2064760394 · doi:10.1097/tp.0b013e318180482d

Non-Melanoma Skin Cancer Incidence and Risk Factors After Kidney Transplantation: A Canadian Experience

2008· article· en· W2064760394 on OpenAlexaffabout
Sherry Comeau, Louise Jensen, Sandra M. Cockfield, Mariusz Sapijaszko, Sita Gourishankar

Bibliographic record

VenueTransplantation · 2008
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineSkin cancerIncidence (geometry)Kidney transplantationTransplantationRetrospective cohort studyCumulative incidenceRisk factorInternal medicineCancer

Abstract

fetched live from OpenAlex

BACKGROUND: Non-melanoma skin cancer (NMSC) after kidney transplantation is common and can result in significant morbidity and mortality. Incidence and risk factors for NMSC can vary between geographic locations and there is no literature describing the incidence or risk factors for NMSC in Canada. METHODS: The purpose of this retrospective cohort study was to determine the incidence of NMSC, the time of development of NMSC, and risk factors (including sun exposure history) for NMSC in kidney transplant recipients between 1990 and 2003 in our center (n=926). RESULTS: We observed a 9.7% incidence of NMSC lesions after kidney transplant with a median time of development of a first NMSC lesion of 4 years. Risk factors for NMSC (multivariate analysis) include older men (>45 years), a history of posttransplant warts, and longer duration of residence in a northern climate. CONCLUSION: We conclude that NMSC is common after kidney transplantation in a northern climate and these individuals require disease prevention-specific education, more vigilant surveillance and early referral and treatment.

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 score0.044
Threshold uncertainty score0.878

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 head0.013
GPT teacher head0.259
Teacher spread0.247 · 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

Citations36
Published2008
Admission routes2
Has abstractyes

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