MétaCan
Menu
← Back to cohort
Record W4417357255 · doi:10.2196/87613

Quantitative Understanding of Advanced Novel Imaging Techniques for Fasciitis and Biosignature Yield (Quantify): Protocol for a Cross-Sectional Diagnostic Study

2025· article· en· W4417357255 on OpenAlexvenueno aff
Zahra Amerian, Timothy R. Fleagle, Utsav Ratna Tuladhar, Rachel Watson, Barbara VanGorp, Brian J Smith, Mederic M. Hall, Jessica Danielson, Renata Vidal Leão, Vincent A. Magnotta, Kathleen A. Sluka, Ruth L. Chimenti

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldMedicine
TopicStreptococcal Infections and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsProtocol (science)Yield (engineering)Medical imagingFasciitisPlantar fasciitis

Abstract

fetched live from OpenAlex

BACKGROUND: Myofascial pain remains an underdiagnosed contributor to musculoskeletal pain conditions, including plantar heel pain, which is the most common source of foot pain. The current standard for diagnosing myofascial pain is a clinical examination using manual palpation. However, this approach lacks quantitative thresholds for precise assessment of myofascial pain, highlighting the need for validated biomarkers. OBJECTIVE: This protocol describes the development of a diagnostic imaging biosignature of myofascial pain using both ultrasound and magnetic resonance imaging to differentiate individuals with plantar heel pain from those with other kinds of foot pain and matched pain-free controls. The study will also explore whether diagnostic accuracy is enhanced by creating a composite biosignature that includes psychological factors. METHODS: In this cross-sectional study, 100 participants will be recruited: 50 with plantar heel pain, 25 with insertional Achilles tendinopathy, and 25 pain-free controls. Participants will undergo a clinical examination of 5 calf and foot muscles to identify sites of abnormal myofascial tissue. The primary imaging outcomes will capture biochemical properties (T1ρ of muscle and fascia), biomechanical properties (shear wave speed of the muscle, shear strain of the plantar fascia during passive movement), and structural profile (fat fraction of the muscle, thickness of the plantar fascia). Patient-reported outcomes will include the National Institutes of Health's Helping to End Addiction Long-term (HEAL) Initiative Common Data Elements and additional psychological measures. RESULTS: This study is supported by grant R61AT012275 from the National Center for Complementary and Integrative Health and the National Institute of Neurological Disorders and Stroke, awarded in September 2024. Participant enrollment began in May 2025. As of November 2025, a total of 55 participants have been enrolled. Enrollment is expected to conclude no later than July 2026. The anticipated study completion date is August 2026. Data will be shared within 1 year of completing the study or upon publication, whichever occurs first. CONCLUSIONS: This protocol provides novel mechanistic insight into myofascial pain through advanced imaging techniques, offering a biopsychosocial framework for improving the diagnosis and treatment of plantar heel pain and related conditions. We anticipate that combining imaging and psychosocial measures will improve the diagnostic accuracy of the biosignature and provide a more comprehensive understanding of myofascial pain. TRIAL REGISTRATION: ClinicalTrials.gov NCT06803056; https://clinicaltrials.gov/study/NCT06803056; OSF Registries osf.io/nxqfj; https://osf.io/nxqfj. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/87613.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.038
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.038
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.036
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0310.008

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.443
GPT teacher head0.633
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreProtocol

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Research Protocols→Same topicStreptococcal Infections and Treatments→French-language works237,207→