Non-operative correction of flat back syndrome using lumbar extension traction: a CBP<sup>®</sup> case series of two
Bibliographic record
Abstract
[Purpose] To document the non-operative rehabilitation of lumbar lordosis in two cases with chronic low back pain and flexible flat back syndrome. [Participants and Methods] Two young adult males reported suffering from chronic low back pain associated with anterior sagittal balance and severe loss of lumbar lordosis, aka 'flat back syndrome.' Lumbar extension traction was applied 3-5 times per week for 16.5-20 weeks. A torsion type lumbar spinal manipulative therapy was provided in the initial 3 weeks for short-term pain relief. [Results] Both patients had dramatic improvement in lumbar lordosis with simultaneous reduction in pain levels. One patient had a 50° lordosis improvement in 100 treatments over 20 weeks; the other had a 26° lordosis improvement in 70 treatments over 16.5 weeks. There were also improvements in sacral base angle, pelvic tilt and sagittal balance. One patient demonstrated stability of health status and further improvements in radiographic measures including lordosis angle nearly 10-months post-treatment. [Conclusion] This is the first successful non-operative correction of flat back syndrome. This approach seems highly effective, is a fraction of the cost of spinal surgery typically used to treat this condition, and offers no health risks including those assumed from radiography necessary for screening and follow-up.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".