Alleviation of shoulder injury related to vaccine administration (SIRVA) pain and disability following COVID-19 vaccine with chiropractic biophysics<sup>®</sup> (CBP<sup>®</sup>) methods: a case report and long-term follow-up with global implications
Bibliographic record
Abstract
[Purpose] To present the dramatic improvement in posture, radiographic parameters and the alleviation of neck and severe shoulder pain related to shoulder injury associated with vaccine administration (SIRVA) after a COVID-19 injection with a shoulder mobility and posture rehabilitation program. [Participant and Methods] A middle-aged male presented complaining of severe left shoulder pain evolving since receiving a COVID-19 vaccination. The pain was severe and throbbed into the neck. Posture analysis showed a chronic stooped posture with forward head posture and thoracic hyperkyphosis. Treatment included 42 sessions of Chiropractic Biophysics® technique and a shoulder rehabilitation program using three-dimensional vibration. [Results] At 4-months, the patient reported no neck or shoulder pain. There was a 60% decrease in neck disability. The forward head decreased 34 mm, thoracic hyperkyphosis decreased 13°, and T1–T12 forward lean decreased 73 mm, among other radiographic parameters. Re-assessment after 26-months showed maintenance of the treatment induced posture/x-ray corrections and shoulder pain relief. [Conclusion] This case demonstrates immediate and long-term improvement in a patient suffering from COVID-19 vaccine SIRVA, concomitant with neck pain and disability as well as significant radiographic postural/spinal deformity. These conditions all improved and were maintained at a 2 year follow-up without further treatment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".