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Record W2043816018 · doi:10.3747/co.v18i2.605

Rash Rates with EGFR Inhibitors: Meta-Analysis

2011· article· en· W2043816018 on OpenAlexaffvenue
Nicole Mittmann, Soo Jin Seung

Bibliographic record

VenueCurrent Oncology · 2011
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Treatments and Studies
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineRashDermatologyAdverse effectInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Currently marketed epidermal growth factor receptor inhibitors (egfris) have been associated with high rates of dermatologic toxicity. METHODS: We formally reviewed the literature at medline and embase. Additional searches were conducted using Internet search engines. Studies were eligible if they were randomized controlled clinical trials of egfris, specifically cetuximab and panitumumab, in which at least one arm consisted of a non-egfri treatment and rash safety data were reported. The random effects method was used to pool differences in incident rash rates. Results are summarized as differences in incident rash (egfri therapy rate minus the non-egfri therapy rate) with corresponding 95% confidence intervals (cis) for all severity grades of rash and for grades 3 and 4 rash. RESULTS: Sixteen studies met the initial inclusion criteria of randomized controlled trials comparing egfri with non-egfri therapy. Seven publications that provided information on all severity grades of rash were found to have an overall difference in incident rash rate of 0.74 (95% ci: 0.68 to 0.81; p < 0.01). Thirteen studies that reported the incidence of grades 3 and 4 rash showed an overall difference in the incident rash rate of 0.12 (95% ci: 0.09 to 0.14; p < 0.01) between egfri and non-egfri therapy. Sensitivity analyses showed that the results were generally robust, but sensitive to small samples. CONCLUSIONS: Results quantify the difference in rash rates between egfri and non-egfri therapy.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.000

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 head0.327
GPT teacher head0.436
Teacher spread0.108 · 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.

Study designMeta-analysis
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

Citations27
Published2011
Admission routes2
Has abstractyes

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