REhabilitation Approaches in CHildren with cerebellar mutism syndrome (REACH): An international cross-disciplinary survey study
Bibliographic record
Abstract
OBJECTIVE: Pediatric cerebellar mutism syndrome (pCMS) can occur following resection of a posterior fossa tumor and, although some symptoms are transient, many result in long-lasting neurological deficits. A multi-disciplinary rehabilitation approach is often used in cases of pCMS; however, there have been no clinical trials to determine gold standards in rehabilitation practice in this population, which remains a research priority. The purpose of this study was to identify and compare intervention practices used in pCMS throughout the disciplines of occupational and physical therapy, speech-language pathology, and neuropsychology across geographic regions. METHODS: A 55-question e-survey was created by an international multidisciplinary research group made up of members of the Posterior Fossa Society and sent to rehabilitation professionals in pediatric neuro-oncology centers in the US, Canada, and Europe. RESULTS: Although some differences in the type of intervention used in pCMS were identified within each discipline, many of the targeted interventions including dose, frequency, and intensity were similar within disciplines across geographic regions. In addition, there were common themes identified across disciplines regarding challenges in the rehabilitation of this population. CONCLUSION: These results provide a foundation of current practices on which to build future intervention-based clinical trials.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".