Rethinking How to Measure the Appropriateness of Cervical Cancer Screening
Bibliographic record
Abstract
Letters19 September 2017Rethinking How to Measure the Appropriateness of Cervical Cancer ScreeningNatasha K. Parekh, MD, MS, Julie M. Donohue, PhD, Aiju Men, MS, Jennifer Corbelli, MD, MS, and Marian Jarlenski, PhD, MPHNatasha K. Parekh, MD, MSFrom University of Pittsburgh, Pittsburgh, Pennsylvania.Search for more papers by this author, Julie M. Donohue, PhDFrom University of Pittsburgh, Pittsburgh, Pennsylvania.Search for more papers by this author, Aiju Men, MSFrom University of Pittsburgh, Pittsburgh, Pennsylvania.Search for more papers by this author, Jennifer Corbelli, MD, MSFrom University of Pittsburgh, Pittsburgh, Pennsylvania.Search for more papers by this author, and Marian Jarlenski, PhD, MPHFrom University of Pittsburgh, Pittsburgh, Pennsylvania.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/L17-0140 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Background: Health care systems use performance measures based on guidelines from such organizations as the American College of Obstetricians and Gynecologists to monitor the appropriateness of cervical cancer screening. According to the performance measure currently in the Healthcare Effectiveness Data and Information Set, satisfactory cervical cancer screening involves at least 1 Papanicolaou (Pap) test every 3 years for average-risk women aged 21 to 64 years or at least 1 Pap and human papillomavirus test every 5 years for average-risk women aged 30 to 64 years (1). These performance measures have notable flaws. They do not allow for brief and clinically ...References1. National Committee for Quality Assurance. Cervical cancer screening. 2017. Accessed at www.ncqa.org/report-cards/health-plans/state-of-health-care-quality/2016-table-of-contents/cervical-cancer-screening on 16 May 2017. Google Scholar2. Sawaya GF, Kulasingam S, Denberg TD, Qaseem A; Clinical Guidelines Committee of American College of Physicians. Cervical cancer screening in average-risk women: best practice advice from the Clinical Guidelines Committee of the American College of Physicians. Ann Intern Med. 2015;162:851-9. [PMID: 25928075]. doi:10.7326/M14-2426 LinkGoogle Scholar3. ACOG Committee on Practice Bulletins—Gynecology. ACOG Practice Bulletin no. 109: cervical cytology screening. Obstet Gynecol. 2009;114:1409-20. [PMID: 20134296] doi:10.1097/AOG.0b013e3181c6f8a4 CrossrefMedlineGoogle Scholar4. Berwick DM, Hackbarth AD. Eliminating waste in US health care. JAMA. 2012;307:1513-6. [PMID: 22419800] doi:10.1001/jama.2012.362 CrossrefMedlineGoogle Scholar5. Smith M, Stuckhardt L, McGinnis JM, eds. Committee on the Learning Health Care System in America; Institute of Medicine. Best Care at Lower Cost: The Path to Continuously Learning Health Care in America. Washington, DC: National Academies Pr; 2013. Google Scholar Author, Article, and Disclosure InformationAffiliations: From University of Pittsburgh, Pittsburgh, Pennsylvania.Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=L17-0140.This article was published at Annals.org on 11 July 2017. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics Cited byEffect of an Electronic Health Record Decision Support Alert to Decrease Excess Cervical Cancer ScreeningCervical cancer screening uptake among HIV-positive women in Ontario, Canada: A population-based retrospective cohort study 19 September 2017Volume 167, Issue 6Page: 445-446KeywordsCervical cancer screeningDisclosureHealth careHealth care qualityHealth information technologyHuman papillomavirusHysterectomyMedicareObstetrics and gynecologyPerformance measures ePublished: 11 July 2017 Issue Published: 19 September 2017 Copyright & PermissionsCopyright © 2017 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.358 | 0.747 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".