Multimodal Fall Risk Prediction in Neurorehabilitation: Preliminary data from an Integrated Model of Clinical, Functional, and Cognitive Parameters with Machine Learning Validation
Bibliographic record
Abstract
We are proud to announce the 5th International Congress on Neurorehabilitation and Neural Repair organized by the Dutch, Belgian and German Societies for Neurorehabilitation and the Association of Chartered Physiotherapists in Neurology in the United Kingdom which will bridge the gap between neuroscience and practice.This 3-day meeting is focused on the most recent advances in neurorehabilitation research ready for translation, providing opportunities to share knowledge, experience, and most recent developments in the identification of biomarker of neuronal recovery, the added value of using innovative devices including robotics in the field of neurorehabilitation.The scientific program will include the most distinguished invited speakers in the field of neuroplasticity and neurorehabilitation, and will be dedicated to the management of most common problems such as gait and balance control, understanding and predicting motor recovery including management of spasticity, cognitive impairments, and implementation strategies of evidence in the field of neurorehabilitation such as stroke, Parkinson's disease and MS.This multidisciplinary conference will be important for all professionals dedicated to neurorehabilitation such as physicians, neurologists, physical and occupational therapists, nurses, movement scientists, bioengineers as well as those who are more involved in the management of neurorehabilitation.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".