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Record W2298234622

Recommendations on routine screening pelvic examination: Canadian Task Force on Preventive Health Care adoption of the American College of Physicians guideline.

2016· article· en· W2298234622 on OpenAlexaffabout
Marcello Tonelli, Sarah Connor Gorber, Ainsley Moore, Brett D. Thombs

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsMcGill University Health CentrePublic Health Agency of CanadaCanadian Institutes of Health Research
Fundersnot available
KeywordsGuidelineMedicinePelvic examinationTask forceAsymptomaticPelvic inflammatory diseaseFamily medicinePhysical examinationHealth carePhysical therapyGynecologySurgeryPathology
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To review the 2014 American College of Physicians (ACP) guideline on the use of pelvic examinations to screen for cancer (other than cervical), pelvic inflammatory disease, or other benign gynecologic conditions to determine whether the ACP guideline on routine pelvic examinations was consistent with Canadian Task Force on Preventive Health Care (CTFPHC) standards and could be adapted or adopted. METHODS: The SNAP-IT (Smooth National Adaptation and Presentation of Guidelines to Improve Thrombosis Treatment) method was used to determine whether the ACP guideline was consistent with CTFPHC standards and could be adapted or adopted. RECOMMENDATIONS: The CTFPHC recommends not performing a screening pelvic examination to screen for noncervical cancer, pelvic inflammatory disease, or other gynecological conditions in asymptomatic women. This is a strong recommendation with moderate-quality evidence. CONCLUSION: The CTFPHC adopts the recommendation on screening pelvic examination as published by the ACP in 2014.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.026
metaresearch head score (Gemma)0.122
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.973
Threshold uncertainty score0.547

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.122
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0100.010
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0080.002
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0080.003

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 head0.035
GPT teacher head0.318
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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

Citations19
Published2016
Admission routes2
Has abstractyes

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Same venuePubMedSame topicCervical Cancer and HPV ResearchFrench-language works237,207