Evidence map›Paper›PMID 37637027›Full record

ArticleGlobal epidemiology2022

Lost in the woods: Finding our way back to the scientific method in systematic review.

Heather N Lynch, Kenneth A Mundt, Dirk Pallapies, Paolo F Ricci

Abstract read
In one paragraph

Article in Global epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Heather N LynchStantec ChemRisk, Boston, MA, United States of America.
Kenneth A MundtStantec ChemRisk, Boston, MA, United States of America.
Dirk PallapiesThe Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Bochum, Germany.
Paolo F RicciUniversity of Bologna, Bologna, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Systematic review has become the preferred approach to addressing causality and informing regulatory and other decision-making processes, including chemical risk assessments. While advocates of systematic reviews acknowledge that they hold great potential for increasing objectivity and transparency in assessments of chemicals and human health risks, standardizing and harmonizing systematic review methods have been challenging. This review provides a brief summary of the development of systematic review methods and some of the frameworks currently in use in the US and Europe. We also provide an in-depth evaluation and comparison of two "competing" US EPA systematic review frameworks, informed by the constructively critical recommendations from the US National Academies of Science, Engineering and Medicine. We conclude with suggestions for moving forward to harmonize systematic review methods, as we believe that further criticism of individual available frameworks likely will be unproductive. Specifically, we issue a call to action for an international collaboration to work toward a blueprint that embraces the most scientifically critical elements common to most systematic review frameworks, while necessarily accommodating adaptations for specific purposes. Despite the array of available systematic review methods, it is clear that there is a shared goal and desire to promote objective assessment and synthesis of scientific evidence informing globally important issues regarding disease causality and human health risk evaluation.

Indexed as

Evidence integrationRegulatory risk assessmentSystematic review

Identifiers

PMID37637027
PMCPMC10445984

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.