Application of Low-Intensity Modified Constraint-Induced Movement Therapy to Improve the Affecter Upper Limb Functionality in Infantile Hemiplegia with Moderate Manual Ability. Case Series
Bibliographic record
Abstract
Objective: To assess the functionality of the affected upper limb in children diagnosed with hemiplegia aged between 4 and 8 years after applying low-intensity modified constraint-induced movement therapy(mCIMT). Methods: Prospective case series study. A mCIMT protocol was applied for five weeks, with two hours of containment per day. The study variables were: quality of movement of the upper limb, spontaneous use, participation of the affected upper limb in activities of daily living, dynamic joint position, grasp-release action, grasp strength, supination and extension elbow movements. Four measurements were performed, using the QUEST scale, the SHUEE Evaluation, a hand dynamometer and a goniometer. Results: The sample was composed of 8 children with moderate manual ability. Statistically significant differences were detected in all the studied variables (p<0.05). The greatest increase occurred in spontaneous use from assessment 1-4 (p = 0.01), reaching 88.87% active participation in bimanual tasks. The quality of movement of the upper limb obtained a significant value due to the increase in dissociated movements and grasp (p = 0.01). Conclusion: A low dose (50 hours) of mCIMT increased the functionality of children diagnosed with congenital hemiplegia between 4 and 8 years of age with moderate manual ability.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".