Evidence map›Paper›PMID 39223573›Full record

ArticleScandinavian journal of trauma, resuscitation and emergency medicine2024

Saving lives with statistics.

Jo Røislien

Abstract readLetter
In one paragraph

Article in Scandinavian journal of trauma, resuscitation and emergency medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Article
  2. Review
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.

Jo RøislienDepartment of Research, The Norwegian Air Ambulance Foundation, Oslo, Norway. jo.roislien@norskluftambulanse.no.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Healthcare is awash with numbers, and figuring out what knowledge these numbers might hold is worthwhile in order to improve patient care. Numbers allow for objective mathematical analysis of the information at hand, but while mathematics is objective by design, our choice of mathematical approach in a given situation is not. In prehospital and critical care, numbers stem from a wide range of different sources and situations, be it experimental setups, observational data or data registries, and what constitutes a "good" statistical analysis can be unclear. A well-crafted statistical analysis can help us see things our eyes cannot, and find patterns where our brains come short, ultimately contributing to changing clinical practice and improving patient outcome. With increasingly more advanced research questions and research designs, traditional statistical approaches are often inadequate, and being able to properly merge statistical competence with clinical knowhow is essential in order to arrive at not only correct, but also valuable and usable research results. By marrying clinical knowhow with rigorous statistical analysis we can accelerate the field of prehospital and critical care.

Indexed as

Critical CareData Interpretation, StatisticalEmergency Medical ServicesHumansPrehospital careStatistical analysisTrauma

Identifiers

PMID39223573
PMCPMC11370087

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.