Management of Lumbar Spinal Stenosis through the Use of Translatoric Manipulation and Lumbar Flexion Exercises: A Case Series
Bibliographic record
Abstract
Lumbar spinal stenosis is a narrowing of the spinal canal or intervertebral foramen that can produce low back pain and leg pain and weakness. Surgical intervention is commonly performed to relieve these symptoms. Symptom reduction and longitudinal management of functional deficits with conservative care is less well documented. The purpose of this case series was to describe the outcomes of a conservative physical therapy program consisting of low- and high-velocity translatoric manipulations of T1-T9 and L1-L3, and two lumbar flexion exercises on 6 subjects diagnosed with lumbar spinal stenosis and neurogenic claudication. A treadmill test was repeated on a weekly basis and at discharge for each patient. All six subjects demonstrated improvements in treadmill walking time prior to the onset of neurogenic claudication (range: 1 min 34 sec to 26 min); in Oswestry Low Back Pain Disability Index scores (range: 7.5% to 64.7%); and in McGill Pain Questionnaire scores (range: 25% to 57%). Five subjects were measured using the Schober technique, and all showed improvement in thoracolumbar flexion mobility. Combined use of translatoric manipulation and spinal flexion exercises may have resulted in improved spinal flexibility, ambulatory abilities, and pain and functional status in six subjects with lumbar spinal stenosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 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".