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Record W2140641884 · doi:10.1158/1078-0432.ccr-09-1476

Vascular Endothelial Growth Factor Concentration as a Predictive Marker: Ready for Primetime?

2010· letter· en· W2140641884 on OpenAlexaff
Peter A. Kavsak, Hal W. Hirte, Sebastién J. Hotte

Bibliographic record

VenueClinical Cancer Research · 2010
Typeletter
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsMcMaster University
Fundersnot available
KeywordsVascular endothelial growth factorMedicineGrowth factorInternal medicineVEGF receptorsReceptor

Abstract

fetched live from OpenAlex

To the Editors: The article by Hanrahan et al. in the May issue of Clinical Cancer Research (1) describes the clinical utility of baseline vascular endothelial growth factor (VEGF) to identify subjects who may benefit from treatment with vandetanib (a VEGF receptor/epidermal growth factor receptor/RET inhibitor; ref. 1). However, important issues, such as preanalytic, analytic, and postanalytic interpretation for VEGF concentrations, need to be addressed before a VEGF cutoff can be “applied to clinical practice” as the authors conclude. First, an important preanalytic issue is the choice of sample type (e.g., matrix). Hanrahan and colleagues indicate that sample type (serum versus EDTA plasma) is a major factor in the concentrations observed, which cannot be easily extrapolated when considering reference intervals and cutoffs. For example, despite using the matrix-specific cutoffs in their study groups (e.g., study groups 3, 6, and 7), only the low VEGF group in study groups 3 and 6 (both with EDTA plasma) had significant lower hazard ratios ( P Second, the analytic performance of the VEGF assay was not reported; only the limit of detection and analytic range were stated. Without knowledge of the precision across the analytic range, it cannot be assumed that adequate performance was obtained at the different cutoffs used in the study. Third, the cutoffs chosen are likely inappropriate. The population where the cutoffs are derived is not characterized (e.g., sex, age, and biochemical indices not provided) and the sample size is too small ( n = 37; ref. 1). Minimally, 120 healthy individuals are required to establish a reference interval, and more if subgrouping is required (3). This type of reference interval study has previously been done for VEGF in EDTA plasma ( n = 304 children; n = 540 adults), which showed that VEGF concentrations are age dependent, with positive associations existing with platelet count, alanine aminotransferase, and oral contraceptive use (4). Unfortunately, Hanrahan et al. (1) did not include these important covariates in their models. We believe that future prospective studies assessing VEGF measurement should incorporate these important preanalytic, analytic, and postanalytic issues. Disclosure of Potential Conflicts of Interest P. Kavsak, commercial research grant and honoraria, Beckman Coulter; commercial research support, Randox Ltd.

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)0.009
metaresearch head score (Gemma)0.051
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.089
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.051
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0050.020
Insufficient payload (model declined to judge)0.0030.000

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 head0.287
GPT teacher head0.555
Teacher spread0.268 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations4
Published2010
Admission routes1
Has abstractyes

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