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Record W6983234680

lncidence and lmplications of Skin Cancers in Chronic Lymphocytic Leukemia (cLL)

2015· other· en· W6983234680 on OpenAlexafffundabout

Bibliographic record

VenueMspace (University of Manitoba) · 2015
Typeother
Languageen
FieldMedicine
TopicBiomedical and Chemical Research
Canadian institutionsManitoba Health
FundersHealth Sciences Centre Research FoundationHeart and Stroke Foundation of Canada
KeywordsChronic lymphocytic leukemiaSkin cancerMerkel cell carcinomaImmunosuppressionBasal cell carcinomaCancerBasal cellLeukemia
DOInot available

Abstract

fetched live from OpenAlex

A recent population-based study in Manitoba showed that skin cancers are very common in chronic lymphocytic leukemia (CLL), probably as a result of immunosuppression. We have now studied 592 newly diagnosed CLL patients attending the CancerCare Manitoba CLL Clinic from 2002 until 2012. The median age at diagnosis of CLL was 67 years (range, 36-99) with a M:F ratio of 1.6.1. The median follow-up was 4.63 years (range, 0.0't-11.00 years). There were 133 (22.39o/o) patients with skin cancers, half having skin cancers before the CLL diagnosis (pre- CLL) and half following the diagnosis (post-CLL). ln the pre-CLL group, the risk of skin cancer increased 5-6 years before the CLL diagnosis indicating that immunosuppression can precede the diagnosis of CLL. For all patients, the risk of skin cancer correlated with Rai stage and duration of disease. Of 368 total skin cancers, 208 (56.520lo) were basal cell carcinomas (BCC), 92 (25.00yo) squamous cell carcinomas (SCC), 47 (12.77o/o) Bowen's disease, 18 (4.89%) melanomas, and 3 (0.82o/o) Merkel cell carcinomas (MCC). Multiple skin cancers occurred in half the patients. 22.72o/o patients died, usually from second malignancies or CLL. There were three deaths from skin cancer, two melanomas and one BCC. ln summary, one-quarter of CLL patients developed skin cancer, and this was predictive for developing a solid tumor. CLL patients, particularly those with advanced Rai stage, require regular surveillance screening for other cancers, especially those of the skin.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.245
Threshold uncertainty score0.999

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 studies0.0000.001
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 head0.024
GPT teacher head0.261
Teacher spread0.237 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2015
Admission routes3
Has abstractyes

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