Evidence map›Paper›PMID 35206893›Full record

ArticleHealthcare (Basel, Switzerland)2022

Common Pregnancy Complaints Can Lead to Motor Vehicle Collisions or Near-Miss Incidents.

Sachi Tsuchikawa, Kyoko Hanahara, Yumiko Tateoka, Masahito Hitosugi

Open access · goldAbstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2022. 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 23% 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, 6 citations in OpenAlex.

  1. Article
  2. Article
  3. 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

4 authors at 2 institutions in 1 country.

Sachi TsuchikawaDepartment of Nursing, Shiga University of Medical Science, Otsu 520-2192, Japan.
Kyoko HanaharaFaculty of Nursing, Seisen University, Hikone 521-1123, Japan.
Yumiko TateokaDepartment of Nursing, Shiga University of Medical Science, Otsu 520-2192, Japan.
Masahito HitosugiDepartment of Legal Medicine, Shiga University of Medical Science, Otsu 520-2192, Japan.ORCID 0000-0001-5243-8766
Shiga University of Medical Science · JPSeisen University · JP

Funding

grants-in-aid for scientific research 19K10683
6 · The paper itself

Abstract

Pregnant women commonly report various health complaints during pregnancy, the occurrence of which is believed to cause human error. However, no study has examined the relationship between the occurrence of pregnancy complaints and the risk of motor vehicle collisions (MVCs). This study aimed to clarify the relationship between the frequency and severity of common pregnancy complaints and the occurrence of MVCs or near-miss incidents. We conducted a multicenter cross-sectional survey of 1000 pregnant women in Shiga Prefecture, Japan. The event group experiencing MVCs or near-miss incidents during pregnancy comprised 10.8% of respondents. The frequency of compression of the stomach or abdomen, tension and cramps in the lower abdomen, pelvic pain, irritability, depressed mood, distractedness, and hot flashes was significantly higher in the event group. The results of our multivariate logistic regression analysis revealed that tension and cramps in the lower abdomen, distractedness, and irritability were independent contributory factors to such events, with an odds ratio of 2.414, 1.849, and 1.746, respectively. Educating pregnant women to avoid driving when experiencing these symptoms would improve maternal and fetal safety.

Indexed as

common complaints in pregnancydrivinghealth guidancemotor vehicle collisionnear-miss incidentpregnant womansafety

Identifiers

PMID35206893
PMCPMC8871814
OpenAlexW4210377100

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.