Evidence map›Paper›PMID 28744318›Full record

ArticleComputational and mathematical methods in medicine2017

Optimization of Classification Strategies of Acetowhite Temporal Patterns towards Improving Diagnostic Performance of Colposcopy.

Karina Gutiérrez-Fragoso, Héctor Gabriel Acosta-Mesa, Nicandro Cruz-Ramírez, Rodolfo Hernández-Jiménez

Open access · hybridAbstract read
In one paragraph

Article in Computational and mathematical methods in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.3field-weighted citation impact, top 35% of its field
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

9 citing papers in PubMed, 22 citations in OpenAlex.

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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

4 authors at 2 institutions in 1 country.

Karina Gutiérrez-FragosoBiomedical Research Center, Universidad Veracruzana, Xalapa, VER, Mexico.ORCID 0000-0002-4968-0188
Héctor Gabriel Acosta-MesaArtificial Intelligence Research Center, Universidad Veracruzana, Xalapa, VER, Mexico.
Nicandro Cruz-RamírezArtificial Intelligence Research Center, Universidad Veracruzana, Xalapa, VER, Mexico.
Rodolfo Hernández-JiménezObstetrician and Gynecologist, Private Practice, Xalapa, VER, Mexico.
Universidad Veracruzana · MXUniversidad de Xalapa · MX

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Efforts have been being made to improve the diagnostic performance of colposcopy, trying to help better diagnose cervical cancer, particularly in developing countries. However, improvements in a number of areas are still necessary, such as the time it takes to process the full digital image of the cervix, the performance of the computing systems used to identify different kinds of tissues, and biopsy sampling. In this paper, we explore three different, well-known automatic classification methods (

Indexed as

Models, StatisticalBayes TheoremCervix UteriColposcopyFemaleHumansPregnancySensitivity and SpecificityUterine Cervical DysplasiaUterine Cervical Neoplasms

Identifiers

PMID28744318
PMCPMC5514345
OpenAlexW2728249558

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.