Maigne Syndrome – A potentially treatable yet underdiagnosed cause of low back pain: A review
Bibliographic record
Abstract
BACKGROUND: First discussed by Dr. Robert Maigne in the late 1980s, Maigne Syndrome is an often unrecognized and treatable cause of low back pain. It can be separated into two distinct entities. The central variant is a result of nerve afferent input secondary to changes of facet joint arthropathy at the thoracolumbar junction. The peripheral variant is a result of impingement of the medial branch of the superior cluneal nerve, which arises from the posterior rami of the lower thoracic and upper lumbar nerve roots, and results in similar clinical symptoms and signs. OBJECTIVE: To review the current literature for a comprehensive description of Maigne Syndrome, its diagnosis and management. METHODS: Evidence was gathered using two main medical databases, namely PubMed and Google Scholar. Search terms included 'Maigne's Syndrome', 'Maigne facet', 'thoracolumbar junction syndrome', 'cluneal nerve entrapment', 'posterior iliac crest trigger point', 'pseudosciatica', as well as various permutations of these terms. RESULTS: The initial search generated 52 articles. These were screened, and duplicate and irrelevant articles were removed. Using the remaining articles, and with evaluation of their cited references, we selected 28 articles for review. Most of these consisted of case reports, many of which were published in rehabilitation, chiropractic and medical journals. The papers explored topics such as anatomy, cluneal nerve imaging, and treatment of nerve entrapment and facet related back pain syndromes, and have been included in this review, which is, to the best our knowledge, the most comprehensive description of Maigne Syndrome to date. CONCLUSION: The keys to the diagnosis of Maigne Syndrome include an awareness of the mechanical causes of back dominant pain, an understanding of the relevant anatomy, a specific clinical examination, and focused radiological guided anesthetic blocks. Treatment is available, and as in all back-pain etiologies, is most effective in the early stages of the disease.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".