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Comparison of APTIMA HPV E6/E7 mRNA and Hybrid Capture 2 Assays using Wet and Dry Self-Collected Flocked Vaginal Swabs and PreservCyt L-Pap Samples

2012· article· en· W2165460976 on OpenAlexaffvenue
Larry Lawson, Alice Lytwyn, D. Jang, Michelle Howard, Laurie Elit, K. Onuma, Michelle Klingel, R. Toor, Jodi Gilchrist, A. Ecobichon-Morris, Marek Smieja, Max Chernesky

Bibliographic record

VenueJournal of Analytical Oncology · 2012
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster University
Fundersnot available
KeywordsMedicineHybrid captureColposcopyGynecologyInternal medicineCervical cancerCancerCervical intraepithelial neoplasia

Abstract

fetched live from OpenAlex

Background: Performance of HPV assays on less invasive specimens can be assessed through agreement of assays and specimen types as well as the ability to identify patients with precancerous lesions. Objectives: To compare the APTIMA HPV (AHPV) E6/E7 mRNA assay to the HC2 DNA test for high risk (HR) HPV performed on PreservCyt L-Pap cervical specimens and flocked self-collected vaginal swabs (SCVS) transported to the laboratory wet or dry. Results: Testing specimens from 100 women attending a colposcopy clinic showed 90.7% (k=0.81) agreement between HC2 and AHPV assays for PreservCyt specimens. Agreement was 80.2% (K=0.80) to 88.0% (K=0.76) between L-Pap and wet and dry SCVS respectively and 89.2% (K=0.77) between the 2 SCVS by AHPV testing. For HC2, the agreement was 90.6% (k=0.81) to 89.2% (k=0.78) between L-Pap and the 2 swabs and 96.0% (k=0.90) between wet and dry swabs. Using pathology (CIN2+) as the reference standard, SCVS tested by AHPV demonstrated sensitivities of 88.8% for dry and 90% for wet SCVS, compared to 86.4% for L-Pap samples. HC2 testing of wet and dry SCVS was 70.8% sensitive compared to 94.4% for L-Pap samples. Conclusion: SCVS collected with flocked nylon swabs transported wet or dry may serve as alternative specimens for HPV testing of women who are reluctant to have a pelvic examination.

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 score0.037
Threshold uncertainty score0.716

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 integrity0.0000.001
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 head0.082
GPT teacher head0.418
Teacher spread0.336 · 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

Citations1
Published2012
Admission routes2
Has abstractyes

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