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Record W2807721810 · doi:10.1111/ajt.14966

Improved keratinocyte carcinoma outcomes with annual dermatology assessment after solid organ transplantation: Population-based cohort study

2018· article· en· W2807721810 on OpenAlexafffundabout
An‐Wen Chan, Kinwah Fung, Peter C. Austin, Soojin Kim, L.G. Singer, Nancy N. Baxter, Raed Alhusayen, Paula A. Rochon

Bibliographic record

VenueAmerican Journal of Transplantation · 2018
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreSt. Michael's HospitalUniversity of TorontoUniversity Health NetworkInstitute for Clinical Evaluative SciencesWomen's College Hospital
FundersCanadian Institutes of Health ResearchOntario Ministry of Health and Long-Term CareInstitute for Clinical Evaluative SciencesCanadian Dermatology Foundation
KeywordsMedicineSkin cancerTransplantationPopulationHazard ratioCohortRetrospective cohort studyQuartileOrgan transplantationCancerInternal medicineDermatologyConfidence intervalEnvironmental health

Abstract

fetched live from OpenAlex

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. 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.

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 score0.009
Threshold uncertainty score0.785

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.006
GPT teacher head0.293
Teacher spread0.287 · 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

Citations41
Published2018
Admission routes3
Has abstractyes

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