Evidence map›Paper›PMID 37061684›Full record

ArticleBMC medical research methodology2023

A new pathway for considering trigger factors based on parallel-serial connection models and displaying the relationships of causal factors in low-probability events.

Liu Hui

Open access · goldAbstract read
In one paragraph

Article in BMC medical research methodology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 47% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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 at 1 institution in 1 country.

Liu HuiCollege of Medical Laboratory, Dalian Medical University, Dalian, 116044, China. liuhui60@sina.com.
Dalian Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo determine the effect size of observed factors considering trigger factors based on parallel-serial models and to explore how multiple factors can be related to the result of complex events for low-probability events with binary outcomes.

methodsA low-probability event with a true binary outcome can be explained by a trigger factor. The models were based on the parallel-serial connection of switches; causal factors, including trigger factors, were simplified as switches. Effect size values of an observed factor for an outcome were calculated as SAR = (Pe-Pn)/(Pe + Pn), where Pe and Pn represent percentages in the exposed and nonexposed groups, respectively, and SAR represents standardized absolute risk. The influence of trigger factors is eliminated by SAR. Actual data were collected to obtain a deeper understanding of the system.

resultsSAR values of < 0.25, 0.25-0.50, and > 0.50 indicate low, medium, and high effect sizes, respectively. The system of data visualization based on the parallel-serial connection model revealed that at least 7 predictors with SAR > 0.50, including a trigger factor, were needed to predict schizophrenia. The SAR of the HLADQB1*03 gene was 0.22 for schizophrenia.

conclusionsIt is likely that the trigger factors and observed factors had a cumulative effect, as indicated by the parallel-serial connection model for binary outcomes. SAR may allow better evaluation of the effect size of a factor in complex events by eliminating the influence of trigger factors. The efficiency and efficacy of observational research could be increased if we are able to clarify how multiple factors can be related to a result in a pragmatic manner.

Indexed as

ProbabilityHumansCase–control studiesCohort studiesData visualisationEffect sizeMultivariate models

Identifiers

PMID37061684
PMCPMC10105445
OpenAlexW4365816212

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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.