Evidence map›Paper›PMID 34027590›Full record

ArticleJournal of digital imaging2021

The Composite Severity Score for Lumbar Spine MRI: a Metric of Cumulative Degenerative Disease Predicts Time Spent on Interpretation and Reporting.

Michael Travis Caton, Walter F Wiggins, Stuart R Pomerantz, Katherine P Andriole

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of digital imaging, 2021. 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

4 authors.

Michael Travis CatonDepartment of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Ave L305, San Francisco, CA, USA. travis.caton@gmail.com.ORCID 0000-0003-1581-7702
Walter F WigginsDepartment of Radiology, Duke University, Durham, NC, 27708, USA.
Stuart R PomerantzDepartment of Radiology, Massachusetts General Hospital, 55 Fruit St, Boston, MA, 02114, USA.
Katherine P AndriolePartners Center for Clinical Data Science, 100 Cambridge Street, Boston, MA, 02114, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Conventional measures of radiologist efficiency, such as the relative value unit, fail to account for variations in the complexity and difficulty of a given study. For lumbar spine MRI (LMRI), an ideal performance metric should account for the global severity of lumbar degenerative disease (LSDD) which may influence reporting time (RT), thereby affecting clinical productivity. This study aims to derive a global LSDD metric and estimate its effect on RT. A 10-year archive of LMRI reports comprising 13,388 exams was reviewed. Objective reporting timestamps were used to calculate RT. A natural language processing (NLP) tool was used to extract radiologist-assigned stenosis severity using a 6-point scale (0 = "normal" to 5 = "severe") at each lumbar level. The composite severity score (CSS) was calculated as the sum of each of 18 stenosis grades. The predictive values of CSS, sex, age, radiologist identity, and referring service on RT were examined with multiple regression models. The NLP tool accurately classified LSDD in 94.8% of cases in a validation set. The CSS increased with patient age and differed between men and women. In a univariable model, CSS was a significant predictor of mean RT (R

Indexed as

Magnetic Resonance ImagingRadiologistsFemaleHumansLumbar VertebraeMale

Identifiers

PMID34027590
PMCPMC8455764

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Registered trials

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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.