Massage for Combat Injuries in Veteran with Undisclosed PTSD: a Retrospective Case Report
Bibliographic record
Abstract
INTRODUCTION: Massage has shown promise in reducing symptoms related to dissociation and anxiety that can exacerbate chronic pain and suffering. The combat wounded, veteran population is increasing and requires a multidisciplinary approach for comprehensive treatment. This case study examines massage therapy use to improve veteran combat injury rehabilitation and recovery experience through purposive, retrospective, and comprehensive SOAP note review. METHODS: A 31-year-old White male received seven, 60-min, full body massages for combat related shoulder injury complications incurred approximately six years before presentation. The right shoulder sustained a broken humeral head and complete dislocation during a defensive maneuver in a life-threatening attack. This case study utilized data from three different assessments: goniometric measurements for shoulder range of motion, observation and documentation for environmental comfort behaviors, and client self-report for treatment goal attainment. Six weekly, full body, 60-min massages were completed sequentially. A follow-up 60-min treatment was completed at Week 8. Treatment to the injured area included focused trigger point therapy, myofascial release, and proprioceptive neuromuscular facilitation to the neck, shoulder, and chest. RESULTS: Total percent change for active flexion, extension, abduction, adduction, internal rotation, and external rotation were 12.5, 150, 40, 167, 14.3, and 0%, respectively. Total percent change for passive flexion, extension, abduction, adduction, internal rotation, and external rotation were 63.6, 350, 66.7, 450, 133, and 77.8%, respectively. Environmental comfort behaviors were reduced. Client treatment goals were attained. CONCLUSIONS: Massage therapy provided meaningful benefit to a combat injury for a veteran with PTSD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".