Utilizing near-infrared spectroscopy to monitor and assess muscle spasticity in children with cerebral palsy undergoing Botox treatment, a feasibility study
Bibliographic record
Abstract
Purpose: Cerebral palsy (CP) is a common pediatric motor disability characterized by motor impairments and spasticity. This study assesses the feasibility of employing near-infrared spectroscopy (NIRS) to objectively evaluate muscle spasticity in children with CP undergoing botulinum toxin type A (BoNT-A) treatment. Methods: Three participants aged 5–16 years with spastic lower limbs were monitored for changes in the tissue oxygenation index (TOI%) and spasticity levels using the Modified Ashworth Scale (MAS) over 6 months. NIRS measurements were collected at baseline and several intervals following BoNT-A administration. Results: Transient increases in TOI% were observed during the first 2–4 weeks post-injection, coinciding with reductions in spasticity. However, by 6 months, TOI% values experienced a moderate decline. Conclusions: This descriptive analysis highlights the potential of NIRS as a non-invasive tool for real-time monitoring of muscle spasticity treatment efficacy. Further studies with larger sample sizes are needed to validate these findings and establish NIRS as a complement to existing clinical tools for muscle spasticity evaluation and management.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".