Photobiomodulation as part of a multi-disciplinary approach for the treatment of Parkinson’s disease symptoms
Bibliographic record
Abstract
Background: Parkinson’s disease is an increasingly common neurodegenerative disease that has a substantial personal, social and economic cost. Although the cardinal symptoms of the disease are motor impairment, non-motor symptoms are arguably just as important for their effect on quality of life. Parkinson’s disease also has a highly heterogeneous symptom presentation. The mainstay of treatment has for many years been levodopa, and more recently dopamine agonists, which can alleviate some of the motor symptoms, but can have severe side-effects, can lose effectiveness over time and do not have an effect on non-motor symptoms. Thus, new treatment modalities are required for management of this disease. Objective: To describe the effect of a multidisciplinary approach for the treatment of the symptoms of Parkinson’s disease, incorporating photobiomodulation (PBM), exercise, diet and careful control of medication. Methods: Participants presented to either a Parkinson’s clinic in Sydney, Australia or to a Parkinson’s rehabilitation centre in Hamilton, Canada. Participants were self-referred, referred by a neurologist, or had been enrolled in a clinical trial and had continued with treatment for two additional years. All participants received PBM therapy to the abdomen and neck and participated in a targeted Parkinson’s specific exercise program. Results: All participants reported improvements in some of their Parkinson’s symptoms, both motor and non-motor, including mobility and gait, balance, fine motor control, cognition, quality of life, and sense of smell. These improvements were maintained over a number of years. Conclusion: A multidisciplinary and individual approach to the treatment of the symptoms of Parkinson’s disease may be an effective way to improve some symptoms and to potentially delay disease progression.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".