Evidence map›Paper›PMID 32011467›Full record

ArticleMedicine2020

Deontological examination as a criterion for the assessment of personal healthcare professional quality: A Strobe compliant retrospective study.

Sandra Mazeikiene, Jurgita Stasiuniene, Diana Vasiljevaite, Sigitas Laima, Sigitas Chmieliauskas, Dmitrij Fomin, Rokas Simakauskas, Algimantas Jasulaitis

Abstract read
In one paragraph

Article in Medicine, 2020. 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
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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.

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

8 authors.

Sandra MazeikieneDepartment of Pathology, Forensic Medicine and Pharmacology, Institute of Biomedical Sciences of the Faculty of Medicine of Vilnius University, Vilnius, Lithuania.
Jurgita Stasiuniene
Diana Vasiljevaite
Sigitas Laima
Sigitas Chmieliauskas
Dmitrij Fomin
Rokas Simakauskas
Algimantas Jasulaitis

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Discrepancies between clinical and autopsy diagnoses range from 30% to 37%. The significance of deontological examinations remains high. In the pursuit of proper evaluation of diagnostic discrepancies, the establishment of pathogenesis, the mechanism of death, and a correct diagnosis are of particular importance.A retrospective study of deontological examinations, aimed at the detection of medical errors and carried out by the State Forensic Medicine Service during the period 1989 to 2016, was performed. The clinical and autopsy data from 1007 cases were collected in compliance with the research protocol.The number of deontological examinations tends to increase. In 60% of cases, the deceased were men. Most cases were in the age group of 50 to 59 years. Most examinations were carried out in relation to improperly provided healthcare services and the patient's death in surgery, admission, intensive care and obstetrics-gynecology departments. In 13% of cases, the diagnosis did not coincide and, in 79% of cases, the diagnoses fully coincided. In 68% of cases, the medical error was disproved.The number of deontological examinations is increasing. In most cases, clinical and autopsy diagnoses fully matched. Incorrectly clinically diagnosed intracranial injuries were the most common diagnostic mistakes. The data are similar to the results of research in other countries and would be relevant to ensuring the prevention of medical mistakes and the improvement of healthcare quality.

Indexed as

Cause of DeathAutopsyDiagnostic ErrorsFemaleForensic MedicineHumansLithuaniaMaleMedical ErrorsQuality of Health CareRetrospective Studies

Identifiers

PMID32011467
PMCPMC7220242

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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.