Use of spinal manipulation in a rheumatoid patient presenting with acute thoracic pain: a case report.
Bibliographic record
Abstract
BACKGROUND: There is limited research related to spinal manipulation of uncomplicated thoracic spine pain and even less when pain is associated with comorbid conditions such as rheumatoid arthritis. In the absence of trial evidence, clinical experience and appropriate selection of the type of intervention is important to informing the appropriate management of these cases. CASE PRESENTATION: We present a case of a patient with long standing rheumatoid arthritis who presented with acute thoracic pain. The patient was diagnosed with costovertebral joint dysfunction and a myofascial strain of the surrounding musculature. The patient was unresponsive to treatment involving a generalized manipulative technique; however, improved following the administration of a specific applied manipulation with modified forces. The patient was deemed recovered and discharged with ergonomic and home care recommendations. DISCUSSION: This case demonstrates a clinical situation where there is a paucity of research to guide management, thus clinicians must rely on experience and patient preferences in the selection of an appropriate and safe therapeutic intervention. The case highlights the need to contextualize the apparent contraindication of manipulation in patients with rheumatoid arthritis and calls for further research. Finally the paper advances evidence based decision making that balances the available research, clinical experience, as well as patient preferences.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".