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Illustrated Instructions for Self-Collection of Anorectal Swab Specimens and Their Adequacy for Cytological Examination

2006· article· en· W2061291594 on OpenAlexaff
Thomas M. Lampinen, Luc LaTulippe, Dirk van Niekerk, Arn Schilder, Mary Lou Miller, Aranka Anema, Robert S. Hogg

Bibliographic record

VenueSexually Transmitted Diseases · 2006
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsUniversity of British ColumbiaBC Cancer AgencyAIDS Vancouver
Fundersnot available
KeywordsMedicineAnal cancerMen who have sex with menAnal sexHuman immunodeficiency virus (HIV)GynecologyAnal canalFamily medicineSurgeryRectumSyphilis

Abstract

fetched live from OpenAlex

In Brief Background: Self-collection of anorectal swab specimens would facilitate screening for anal cancer precursors and sexually transmitted rectal infections among men who have sex with men (MSM). However, pictorial guides for self-collection were not previously available. Goals: Develop and field test a set of illustrated self-collection instructions. Design: Cross-sectional study of community-recruited MSM who were naïve with regard to collection of specimens for anal cytology. Results: Among 222 self- and clinician-collected swab pairs provided by mostly human immunodeficiency virus (HIV)-1 seronegative MSM (median age, 31.5 years), most specimens were adequate for cytologic evaluation, though self-collected swabs were less likely to be so (83% versus 92%, P = <0.001). The illustrated instructions were reportedly essential, but having used them, men rated their understanding of the self-collection procedure as very high. Conclusions: Provided with illustrated instructions, most MSM who are naïve to the technique can self-collect anorectal swab specimens that are suitable for screening. These illustrated instructions for self-collection of anorectal swab specimens may facilitate screening for anal cancer precursors and sexually transmitted bacterial infections in men who have sex with men.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.889
Threshold uncertainty score0.451

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 studies0.0000.000
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 head0.020
GPT teacher head0.284
Teacher spread0.265 · 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

Citations35
Published2006
Admission routes1
Has abstractyes

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