Evidence map›Paper›PMID 37486137›Full record

ArticleJournal of visualized experiments : JoVE2023

Quantifying Pain Location and Intensity with Multimodal Pain Body Diagrams.

Jereen Kwong, Joanna Lin, Ryan Leriche, Thomas A Wozny, Ana Shaughnessy, Ashlyn Schmitgen, Prasad Shirvalkar

Abstract readVideo-Audio Media
In one paragraph

Article in Journal of visualized experiments : JoVE, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Predicting pain location from resting-state brain fMRI.bioRxiv : the preprint server for biology · 2026
    Article
  3. Article
  4. 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

7 authors.

Jereen Kwong *Anesthesiology (Division of Pain Management), University of California, San Francisco.
Joanna Lin *Neurological Surgery and UCSF Weill Institute of Neurosciences, University of California, San Francisco.
Ryan Leriche *Neurological Surgery and UCSF Weill Institute of Neurosciences, University of California, San Francisco.
Thomas A WoznyNeurological Surgery and UCSF Weill Institute of Neurosciences, University of California, San Francisco.
Ana ShaughnessyNeurological Surgery and UCSF Weill Institute of Neurosciences, University of California, San Francisco.
Ashlyn SchmitgenNeurological Surgery and UCSF Weill Institute of Neurosciences, University of California, San Francisco.
Prasad ShirvalkarAnesthesiology (Division of Pain Management) and Neurological Surgery and UCSF Weill Institute of Neurosciences, University of California, San Francisco; Prasad.Shirvalkar@ucsf.edu.

Funding

Multisite adaptive brain stimulation for multidimensional treatment of refractory chronic painUH3NS115631 · NINDS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI SHIRVALKAR, PRASAD · 2019 to 2023
$6.9M
NINDS NIH HHS UH3 NS115631
6 · The paper itself

Abstract

To quantify an individual's subjective pain severity, standardized pain rating scales such as the numeric rating scale (NRS), visual analog scale (VAS), or McGill pain questionnaire (MPQ) are commonly used to assess pain on a numerical scale. However, these scales are often biased and fail to capture the complexity of pain experiences. In contrast, clinical practice often requires patients to report areas of pain by drawing on a body diagram, which is an effective but qualitative tool. The method presented here extracts quantifiable metrics from pain body diagrams (PBDs) which are validated against the NRS, VAS, and MPQ pain scales. By using a novel pressure-hue transformation on a digital tablet, different drawing pressures applied with a digital stylus can be represented as different hues on a PBD. This produces a visually intuitive diagram of hues ranging from green to blue to red, representing mild to moderate to most painful regions, respectively. To quantify each PBD, novel pain metrics were defined: (1) PBD mean intensity, which equals the sum of each pixel's hue value divided by the number of colored pixels, (2) PBD coverage, which equals the number of colored pixels divided by the total number of pixels on the body, and (3) PBD sum intensity, which equals the sum of all pixels' hue values. Using correlation and information theory analyses, these PBD metrics were shown to have high concordance with standardized pain metrics, including NRS, VAS and MPQ. In conclusion, PBDs can provide novel spatial and quantitative information that can be repeatedly measured and tracked over time to comprehensively characterize a participant's pain experience.

Indexed as

PainHumansPain MeasurementVisual Analog Scale

Identifiers

PMID37486137
PMCPMC12721584

What OpenQuestion holds

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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.