Evidence map›Paper›PMID 38347647›Full record

ArticleDiagnostic and prognostic research2024

Risk prediction models for lung cancer in people who have never smoked: a protocol of a systematic review.

Alpamys Issanov, Atul Aravindakshan, Lorri Puil, Martin C Tammemägi, Stephen Lam, Trevor J B Dummer

Open access · goldAbstract read
In one paragraph

Article in Diagnostic and prognostic research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
3.8field-weighted citation impact, top 7% 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Review
  5. Review
  6. Review
  7. 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

6 authors at 2 institutions in 1 country.

Alpamys IssanovSchool of Population and Public Health, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada. alpamys.issanov@ubc.ca.ORCID http://orcid.org/0000-0002-8968-2655
Atul AravindakshanSchool of Population and Public Health, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada.
Lorri PuilSchool of Population and Public Health, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada.
Martin C TammemägiFaculty of Applied Health Sciences, Brock University, St. Catharines, ON, Canada.
Stephen LamBC Cancer, Provincial Health Services Authority, Vancouver, BC, Canada.
Trevor J B DummerSchool of Population and Public Health, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada.
University of British Columbia · CABrock University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLung cancer is one of the most commonly diagnosed cancers and the leading cause of cancer-related death worldwide. Although smoking is the primary cause of the cancer, lung cancer is also commonly diagnosed in people who have never smoked. Currently, the proportion of people who have never smoked diagnosed with lung cancer is increasing. Despite this alarming trend, this population is ineligible for lung screening. With the increasing proportion of people who have never smoked among lung cancer cases, there is a pressing need to develop prediction models to identify high-risk people who have never smoked and include them in lung cancer screening programs. Thus, our systematic review is intended to provide a comprehensive summary of the evidence on existing risk prediction models for lung cancer in people who have never smoked.

methodsElectronic searches will be conducted in MEDLINE (Ovid), Embase (Ovid), Web of Science Core Collection (Clarivate Analytics), Scopus, and Europe PMC and Open-Access Theses and Dissertations databases. Two reviewers will independently perform title and abstract screening, full-text review, and data extraction using the Covidence review platform. Data extraction will be performed based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS). The risk of bias will be evaluated independently by two reviewers using the Prediction model Risk-of-Bias Assessment Tool (PROBAST) tool. If a sufficient number of studies are identified to have externally validated the same prediction model, we will combine model performance measures to evaluate the model's average predictive accuracy (e.g., calibration, discrimination) across diverse settings and populations and explore sources of heterogeneity. DISCUSSION: The results of the review will identify risk prediction models for lung cancer in people who have never smoked. These will be useful for researchers planning to develop novel prediction models, and for clinical practitioners and policy makers seeking guidance for clinical decision-making and the formulation of future lung cancer screening strategies for people who have never smoked. SYSTEMATIC REVIEW REGISTRATION: This protocol has been registered in PROSPERO under the registration number CRD42023483824.

Indexed as

Lung cancerNever smokersPrediction modelSystematic review

Identifiers

PMID38347647
PMCPMC10863273
OpenAlexW4392729978

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.