Evidence map›Paper›PMID 37325887›Full record

ReviewCancer medicine2023

Innovations in healthcare delivery: Human papilloma virus self sampling diagnostics and participatory innovations for CCS.

Evans Appiah Osei

Abstract readReview
In one paragraph

Review in Cancer medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Evans Appiah OseiPurdue University, West Lafayette, Indiana, USA.ORCID 0000-0002-6730-4725

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHuman papillomavirus (HPV) infection is a major contributor to the development of cervical cancer, resulting in over 500,000 cases and 266,000 deaths annually worldwide. Previous cervical cancer screening programs have been successful in reducing cervical cancer rates, but have faced challenges such as low acceptance and adherence rates. Innovations in screening technology, such as the HerSwab self-sampling test, have the potential to increase awareness, acceptance, and participation in cervical cancer screening programs.

aimThis literature review examines the effectiveness of HerSwab and participatory innovations in increasing adherence to cervical cancer screening.

methodThis manuscript comprised a comprehensive narrative literature review encompassing the years 2006-2022. The review process adhered to the PRISMA diagram as a guiding framework. Among the search terms utilized, a total of 200 articles were initially retrieved. However, after applying the predefined inclusion criteria, only 57 articles were included.

resultsThe HerSwab self-sampling test is described, including how it is performed, challenges, and facilitators, and evaluation and assessment of its effectiveness. While the HerSwab diagnostic test is not currently widely available, studies should assess its feasibility in less developed countries where cervical cancer mortality rates are high.

conclusionBy increasing awareness and availability of innovative screening techniques, such as HerSwab, we can work toward reducing the incidence of cervical cancer and improving outcomes for women worldwide.

Indexed as

Papillomavirus InfectionsUterine Cervical NeoplasmsCervix UteriDelivery of Health CareEarly Detection of CancerFemaleHuman Papillomavirus VirusesHumansMass ScreeningPapillomaviridaecervical cancer screeningHerSwabhuman papilloma virusinnovationsparticipatory

Identifiers

PMID37325887
PMCPMC10417092

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.