Evidence map›Paper›PMID 32869763›Full record

ArticleLa Medicina del lavoro2020

Criteria for diagnosis and attribution of an occupational musculoskeletal disease.

Francesco Saverio Violante

Abstract read
In one paragraph

Article in La Medicina del lavoro, 2020. 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
–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

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

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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.

Francesco Saverio ViolanteDepartment of Medical and Surgical Sciences (DIMEC), Alma Mater Studiorum University of Bologna; Occupational Health Unit University of Bologna and Sant'Orsola Malpighi Hospital, Bologna, Italy. francesco.violante@unibo.it.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCriteria for diagnosis and compensation of occupational musculoskeletal diseases varies widely between countries as demonstrated by the large differences between countries with comparable economics and social systems (for example, within the European Union). Several countries have a list of occupational diseases and sometimes these lists include diagnostic and attribution criteria, but these criteria are usually not very specific, and they may also be very different.

objectivesThe aim of this paper is to explicitly define what are the information needed for an evidence-based diagnosis and attribution of an occupational musculoskeletal disease.

methodsBased on the general framework of evidence-based medicine, a review is presented of the information required to define: - when a musculoskeletal disease is present, according to the best available techniques; - how to define a relevant exposure to biomechanical risk factors, according to the best available techniques.

resultsCriteria are presented to combine information regarding the diagnosis of a musculoskeletal disease and exposure to biomechanical risk factors for an evidence-based attribution of the disease to the occupational exposure. The criteria use a probabilistic model that combine epidemiologic and medical findings, workplace exposure assessment, and non-occupational factors evaluation. DISCUSSION: The use of the proposed criteria may improve the process of diagnosis and attribution of an occupational musculoskeletal disease. In addition, it makes possible to associate a probability rank to the attribution and, ultimately, it may improve the overall quality of the decisional process of the occupational physician.

Indexed as

Musculoskeletal DiseasesOccupational DiseasesOccupational ExposureHumansRisk FactorsWorkplace

Identifiers

PMID32869763
PMCPMC7809956

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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.