Evidence map›Paper›PMID 35474718›Full record

ArticleJAMIA open2022

Representing narrative evidence as clinical evidence logic statements.

Ronilda Lacson, Mahsa Eskian, Laila Cochon, Isha Gujrathi, Andro Licaros, Anna Zhao, Nicole Vetrano, Louise Schneider, Ali Raja, Ramin Khorasani

Abstract read
In one paragraph

Article in JAMIA open, 2022. 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
–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

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

10 authors.

Ronilda LacsonDepartment of Radiology, Center for Evidence-Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts, USA.ORCID https://orcid.org/0000-0003-3300-1176
Mahsa EskianDepartment of Radiology, Center for Evidence-Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Laila CochonDepartment of Radiology, Center for Evidence-Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Isha GujrathiDepartment of Radiology, Center for Evidence-Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Andro LicarosDepartment of Radiology, Center for Evidence-Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Anna ZhaoDepartment of Radiology, Center for Evidence-Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Nicole VetranoDepartment of Radiology, Center for Evidence-Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Louise SchneiderDepartment of Radiology, Center for Evidence-Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Ali RajaDepartment of Radiology, Center for Evidence-Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Ramin KhorasaniDepartment of Radiology, Center for Evidence-Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Clinical evidence logic statements (CELS) are shareable knowledge artifacts in a semistructured "If-Then" format that can be used for clinical decision support systems. This project aimed to assess factors facilitating CELS representation. Materials and Methods: We described CELS representation of clinical evidence. We assessed factors that facilitate representation, including authoring instruction, evidence structure, and educational level of CELS authors. Five researchers were tasked with representing CELS from published evidence. Represented CELS were compared with the formal representation. After an authoring instruction intervention, the same researchers were asked to represent the same CELS and accuracy was compared with that preintervention using McNemar's test. Moreover, CELS representation accuracy was compared between evidence that is structured versus semistructured, and between CELS authored by specialty-trained versus nonspecialty-trained researchers, using χ Results: 261 CELS were represented from 10 different pieces of published evidence by the researchers pre- and postintervention. CELS representation accuracy significantly increased post-intervention, from 20/261 (8%) to 63/261 (24%, Discussion: CELS represented from structured evidence had a higher representation accuracy compared with semistructured evidence. Similarly, specialty-trained authors had higher accuracy when representing structured evidence. Conclusion: Authoring instructions significantly improved CELS representation with a 3-fold increase in accuracy. However, CELS representation remains a challenging task.

Indexed as

clinical decision supporthealth information technologyknowledge representation

Identifiers

PMID35474718
PMCPMC9030217

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LicenceCC BY
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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.