Evidence map›Paper›PMID 34026714›Full record

SynthesisFrontiers in public health2021

Prediction Models for Prognosis of Cervical Cancer: Systematic Review and Critical Appraisal.

Bingjie He, Weiye Chen, Lili Liu, Zheng Hou, Haiyan Zhu, Haozhe Cheng, Yixi Zhang, Siyan Zhan, Shengfeng Wang

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Observational
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
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

9 authors.

Bingjie HeDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China.
Weiye ChenDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China.
Lili LiuDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China.
Zheng HouDepartment of Obsterics and Gynecology, Peking University Third Hospital, Beijing, China.
Haiyan ZhuSchool of Public Health, Peking University Health Science Center, Beijing, China.
Haozhe ChengSchool of Public Health, Peking University Health Science Center, Beijing, China.
Yixi ZhangSchool of Public Health, Peking University Health Science Center, Beijing, China.
Siyan ZhanDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China.
Shengfeng WangDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

Uterine Cervical NeoplasmsBiasFemaleHumansPregnancyPrognosiscervical cancerprediction modelpredictorsrisk of biasstatistical analysis

Identifiers

PMID34026714
PMCPMC8137851

What OpenQuestion holds

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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.