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Noncutaneous and Cutaneous Cancer Risk in Patients With Atopic Dermatitis

2019· review· en· W2995130207 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJAMA Dermatology · 2019
Typereview
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsMcGill University Health CentreWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineAtopic dermatitisObservational studyPopulationOdds ratioInternal medicineSkin cancerIncidence (geometry)CancerMeta-analysisDermatologyOncologyEnvironmental health

Abstract

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Importance: Impaired skin barrier and aberrant immune function in atopic dermatitis (AD) may alter immune response to malignant cancer. Conflicting data exist on the risk of cancer in patients with AD. Objective: To assess the risk of noncutaneous and cutaneous cancers in patients with AD compared with the general population without AD. Data Sources: Studies identified from searches of MEDLINE and Embase that were published from 1946 and 1980, respectively, to January 3, 2019. The following search terms were used: [(exp NEOPLASMS/ OR neoplas*.tw. OR tumo*.tw. OR cancer*.tw. OR malignanc*.tw.) AND (exp Dermatitis, Atopic/ OR (atopic adj1 (dermatit* or neurodermatit*)).tw. OR eczema.tw. OR disseminated OR neurodermatit*.tw.)]. Study Selection: Included were observational studies (cohort and case-control designs) reporting a risk estimate for cancer in patients with AD compared with a control group (general population or patients without AD). Data Extraction and Synthesis: Two independent reviewers extracted data and assessed the risk of bias using the Risk of Bias in Nonrandomized Studies of Interventions (ROBINS-I) assessment tool, modified for observational exposure studies. Data were pooled using a random-effects model and expressed as standardized incidence ratios (SIRs) or odds ratios (ORs) with 95% CIs. Heterogeneity was assessed using the Cochrane Q statistic and the I2 statistic. Main Outcomes and Measures: The main outcome of the study was risk of cancer measured by SIRs or ORs. Results: This systematic review and meta-analysis included 8 population-based cohort studies (n = 5 726 692 participants) and 48 case-control studies (n = 114 136 participants). Among cohort studies, a statistically significant association was found between AD and keratinocyte carcinoma (5 studies; pooled SIR, 1.46; 95% CI, 1.20-1.77) as well as cancers of the kidney (2 studies; pooled SIR, 1.86; 95% CI, 1.14-3.04), central nervous system (2 studies; pooled SIR, 1.81; 95% CI, 1.22-2.70), and pancreas (1 study; SIR, 1.90; 95% CI, 1.03-3.50). Among 48 case-control studies, pooled effects showed patients with AD had statistically significantly lower odds of central nervous system cancers (15 studies; pooled OR, 0.76; 95% CI, 0.70-0.82) and pancreatic cancer (5 studies; pooled OR, 0.81; 95% CI, 0.66-0.98), contrary to the higher incidence found in cohort studies. Case-control studies also demonstrated lower odds of lung and respiratory system cancers (4 studies; pooled OR, 0.61; 95% CI, 0.45-0.82). No evidence of association was found between AD and other cancer types, including melanoma. There was substantial heterogeneity between studies for many other cancers, which precluded pooling of data, and there was moderate to serious risk of bias among included studies. Conclusions and Relevance: Observational evidence suggests potential associations between AD and increased risk of keratinocyte carcinoma and kidney cancer as well as lower odds of lung and respiratory system cancers. Further research is needed to address the heterogeneity and limitations of current evidence and to better understand the mechanisms underlying a possible association between AD and cancer risk.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.897
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.012
GPT teacher head0.280
Teacher spread0.269 · 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