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Record W4417152938 · doi:10.3822/ijtmb.v18i4.1211

A Commentary on the Potential Impact of Massage Therapy on Military Veterans

2025· article· en· W4417152938 on OpenAlexvenueaboutno aff
Cruz N. Rodrı́guez, Jude Matyo-Cepero

Bibliographic record

VenueInternational Journal of Therapeutic Massage & Bodywork Research Education & Practice · 2025
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMassageVeterans AffairsGovernment (linguistics)Alternative medicineMilitary personnelMilitary medicine

Abstract

fetched live from OpenAlex

Professionals in the massage therapy field strive to help clients gain pain relief. Military veterans are a small group that experiences significant pain due to service-related physical and mental disabilities. The struggle of these veterans in the United States and Canada has been a concern after decades of combat overseas and rigorous training stateside. Nearly a quarter century after the September 11 attacks, government organizations such as the medical branch of the Department of Veterans Affairs (VA) in the United States have recognized the benefits of massage therapy for veterans. The VA has gone as far as paying for massage therapy treatments at no cost to the veteran. This commentary provides a brief overview of the physical and mental disabilities faced by U.S. and Canadian military veterans, an example of the VA in the U.S. supporting veterans with massage therapy, and considerations for the future.

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)0.008
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.050
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.055
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.002
Science and technology studies0.0090.007
Scholarly communication0.0040.007
Open science0.0050.002
Research integrity0.0500.048
Insufficient payload (model declined to judge)0.0070.003

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 head0.096
GPT teacher head0.516
Teacher spread0.420 · 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 designNot applicable
Domainnot available
GenreCommentary

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 routes2
Has abstractyes

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