Evidence map›Paper›PMID 33490848›Full record

ArticlePain reports

Development and validation of the Collaborative Health Outcomes Information Registry body map.

Kristen Hymel Scherrer, Maisa S Ziadni, Jiang-Ti Kong, John A Sturgeon, Vafi Salmasi, Juliette Hong, Eric Cramer, Abby L Chen, Teresa Pacht, Garrick Olson and 3 more

Open access · goldAbstract read
In one paragraph

Article in Pain reports. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.

0numbers the graph read from it
0cells of the map it votes in
56citing papers in PubMed
7.3field-weighted citation impact, top 2% of its field
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

56 citing papers in PubMed, 71 citations in OpenAlex.

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  6. Predicting pain location from resting-state brain fMRI.bioRxiv : the preprint server for biology · 2026
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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

13 authors at 3 institutions in 1 country.

Kristen Hymel ScherrerDivision of Pain Management, Stanford University School of Medicine, Palo Alto, CA, USA.
Maisa S ZiadniDivision of Pain Management, Stanford University School of Medicine, Palo Alto, CA, USA.
Jiang-Ti KongDivision of Pain Management, Stanford University School of Medicine, Palo Alto, CA, USA.
John A SturgeonDepartment of Anesthesiology and Pain Medicine, University of Washington Medical School, Seattle, WA, USA.
Vafi SalmasiDivision of Pain Management, Stanford University School of Medicine, Palo Alto, CA, USA.
Juliette HongDivision of Pain Management, Stanford University School of Medicine, Palo Alto, CA, USA.
Eric CramerDivision of Pain Management, Stanford University School of Medicine, Palo Alto, CA, USA.
Abby L ChenDivision of Pain Management, Stanford University School of Medicine, Palo Alto, CA, USA.
Teresa PachtDivision of Pain Management, Stanford University School of Medicine, Palo Alto, CA, USA.
Garrick OlsonDivision of Pain Management, Stanford University School of Medicine, Palo Alto, CA, USA.
Beth D DarnallDivision of Pain Management, Stanford University School of Medicine, Palo Alto, CA, USA.
Ming-Chih KaoDivision of Pain Management, Stanford University School of Medicine, Palo Alto, CA, USA.
Sean MackeyDivision of Pain Management, Stanford University School of Medicine, Palo Alto, CA, USA.
Stanford University · USUniversity of North Carolina at Chapel Hill · USUniversity of Washington Medical Center · US

Funding

Stanford CAM Center for Chronic Back PainP01AT006651 · NCCIH · STANFORD UNIVERSITY · PI MACKEY, SEAN C · 2011 to 2018
$8.2M
Interdisciplinary Research Training in Pain and Substance Use DisordersT32DA035165 · NIDA · STANFORD UNIVERSITY · PI SEAN C MACKEY · 2013 to 2026
$7.2M
Single Session Pain Catastrophizing Treatment: Comparative Efficacy & MechanismsR01AT008561 · NCCIH · STANFORD UNIVERSITY · PI DARNALL, BETH DENISE, MACKEY, SEAN C · 2015 to 2019
$3.9M
Neuroimaging and Mentoring in Translational Pain ResearchK24DA029262 · NIDA · STANFORD UNIVERSITY · PI MACKEY, SEAN C · 2010 to 2019
$1.9M
Single Session Pain Catastrophizing Class: Efficacy & Mechanisms for Reducing Opioid Use Among Chronic Pain PatientsK23DA047473 · NIDA · STANFORD UNIVERSITY · PI ZIADNI, MAISA · 2019 to 2023
$796k
Predicting Analgesic Response to Acupuncture - A Practical ApproachK23AT008477 · NCCIH · STANFORD UNIVERSITY · PI KONG, JIANG-TI · 2015 to 2019
$739k
NCCIH NIH HHS K23 AT008477NCCIH NIH HHS P01 AT006651NCCIH NIH HHS R01 AT008561NIDA NIH HHS K23 DA047473NIDA NIH HHS K24 DA029262NIDA NIH HHS T32 DA035165
6 · The paper itself

Abstract

introductionCritical for the diagnosis and treatment of chronic pain is the anatomical distribution of pain. Several body maps allow patients to indicate pain areas on paper; however, each has its limitations.

objectivesTo provide a comprehensive body map that can be universally applied across pain conditions, we developed the electronic Collaborative Health Outcomes Information Registry (CHOIR) self-report body map by performing an environmental scan and assessing existing body maps.

methodsAfter initial validation using a Delphi technique, we compared (1) pain location questionnaire responses of 530 participants with chronic pain with (2) their pain endorsements on the CHOIR body map (CBM) graphic. A subset of participants (n = 278) repeated the survey 1 week later to assess test-retest reliability. Finally, we interviewed a patient cohort from a tertiary pain management clinic (n = 28) to identify reasons for endorsement discordances.

resultsThe intraclass correlation coefficient between the total number of body areas endorsed on the survey and those from the body map was 0.86 and improved to 0.93 at follow-up. The intraclass correlation coefficient of the 2 body map graphics separated by 1 week was 0.93. Further examination demonstrated high consistency between the questionnaire and CBM graphic (<10% discordance) in most body areas except for the back and shoulders (≈15-19% discordance). Participants attributed inconsistencies to misinterpretation of body regions and laterality, the latter of which was addressed by modifying the instructions.

conclusionsOur data suggest that the CBM is a valid and reliable instrument for assessing the distribution of pain.

Indexed as

Body mapCHOIRChronic painDevelopmentValidation

Identifiers

PMID33490848
PMCPMC7813550
OpenAlexW3124781960

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.