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Record W2521334984 · doi:10.1177/1203475416671694

One-Year Review of the SCREEN (Skin Cancer Post-Transplant) Clinic

2016· article· en· W2521334984 on OpenAlexaffabout
Sarah Baldwin, Sheila Au

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2016
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsSt. Paul's HospitalProvidence Health CareUniversity of British Columbia
Fundersnot available
KeywordsMedicineSkin cancerDermatologyCancerInternal medicine

Abstract

fetched live from OpenAlex

The Skin Cancer Post-Transplant (SCREEN) Clinic is a skin-cancer screening clinic that is fully integrated into the renal transplantation clinic at St Paul's Hospital in Vancouver, British Columbia. The purpose of this review was to determine characteristics of patients most at risk for skin cancer, to specify types and locations of skin cancers diagnosed, and to identify areas for patient and physician education. Transplant patients (91% renal; 5% heart) screened by a dermatologist during a 12-month period were stratified into low-, medium-, and high-risk groups based on detailed history and skin examination. In total, 118 skin cancers were diagnosed. White males were found to be most at risk. Squamous cell carcinoma accounted for the majority of tumours, over 25% of which demonstrated invasion. Forty-two percent of patients used only 1 to 2 bottles of sunscreen per year, and sun-protective practices were limited. With this information, we have identified potential target areas for patient and physician education.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.381
Threshold uncertainty score0.925

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.037
GPT teacher head0.308
Teacher spread0.272 · 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

Citations7
Published2016
Admission routes2
Has abstractyes

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