Resolution of Cavitational Osteonecrosis Through NeuroModulation Technique, a Novel Form of Intention-Based Therapy: A Clinical Case Study
Bibliographic record
Abstract
OBJECTIVES: This study evaluated the possibility of using NeuroModulation Technique (NMT), a form of intention-based medicine, to induce osteogenesis and healing of cavitational osteonecrosis, a common progressive form of ischemic disease of the alveolar arch. DESIGN: Eleven (11) adult patients were enrolled based on the presence of lesions in the jawbone. Ten (10) subjects underwent NMT therapy for up to 10 months, while 1 subject received no treatment. OUTCOME MEASURES: A sensitive analysis of bone density in the alveolar processes of maxilla and mandible was performed before and after therapy using the U.S. Food and Drug Administration-approved Cavitat system of through-transmission ultrasonography and computer imaging. RESULTS: All subjects presented between one and six cavitational lesions at the first scan, most of which (92%) were associated with sites of previous tooth extraction. NMT-treated patients demonstrated significant improvement in bone density in 27 of the 34 lesions analyzed (79%). The median number of lesions per patient was 4 pretreatment and 0 post-treatment (p < 0.01). One NMT-treated patient, 1 surgically treated patient, and the control subject were also imaged at later time points, showing a durable healing of the lesions through NMT comparable to that of surgery, as opposed to disease persistence in the untreated control. CONCLUSIONS: NMT therapy provides a safe and potentially effective treatment for jawbone osteonecrosis. Preclinical placebo-controlled trials are encouraged to investigate in depth the potential of NMT for treating inflammatory and degenerative pathologies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 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".