Evidence map›Paper›PMID 38148909›Full record

ArticleFrontiers in medicine2023

A predictive model for residual lesions after LEEP surgery in CIN III patients.

Lihui Deng, Tiejun Wang, Ye Chen, Xueli Tang, Dajun Xiang

Open access · goldAbstract read
In one paragraph

Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.2field-weighted citation impact, top 19% 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

3 citing papers in PubMed, 4 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

5 authors at 1 institution in 1 country.

Lihui DengDepartment of Gynecology, Xishan People's Hospital of Wuxi City, Wuxi, China.
Tiejun WangDepartment of Gynecological Oncology, Wuxi Maternal and Child Health Hospital, Wuxi, China.
Ye ChenDepartment of Gynecology, Xishan People's Hospital of Wuxi City, Wuxi, China.
Xueli TangDepartment of Gynecology, Xishan People's Hospital of Wuxi City, Wuxi, China.
Dajun XiangDepartment of Gynecology, Xishan People's Hospital of Wuxi City, Wuxi, China.
Wuxi People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and aims: The residual lesions after Loop Electrosurgical Excision Procedure (LEEP) contributes to poor prognosis in patients with Cervical Intraepithelial Neoplasia Grade 3 (CIN3). The aim of this study is to establish an effective clinical predictive model for residual lesions in CIN3 patients after LEEP. Methods: A retrospective analysis was performed on 436 CIN3 patients who underwent total hysterectomy within 3 months after LEEP. Based on the post-hysterectomy pathologic, the patients were divided into the no residual group and residual group. Clinical parameters were compared between the two groups, and univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for residual lesions in CIN3 patients after LEEP. Using R software, a nomogram model was established and its effectiveness was evaluated using calibration plots. Results: There were 178 cases in the residual group and 258 cases in the no residual group. The two groups had no significant difference in general characteristics ( Conclusion: The Post-LEEP follow-up HPV, Post-LEEP follow-up TCT, and Gland involvement are independent risk factors related to residual tissue after LEEP surgery in CIN3 patients. The constructed nomogram can effectively predict the presence of residual tissue after LEEP surgery in CIN3 patients and has good practical value.

Indexed as

cervical intraepithelial neoplasia grade IIILEEPnomogram LEEPpost-total hysterectomypredictive model

Identifiers

PMID38148909
PMCPMC10751019
OpenAlexW4389572476

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.