Whom to Target for Falls-Prevention Trials
Bibliographic record
Abstract
Effective falls-prevention approaches for people with multiple sclerosis (MS) are needed. A significant challenge in studying falls-prevention programs for people with MS is deciding whom to include in trials. This article presents and discusses potential criteria for selecting participants for trials of falls-prevention interventions in MS. This narrative review reports on the inaugural meeting of the International MS Falls Prevention Research Network (IMSFPRN), which was held in March 2014 in Kingston, Ontario, Canada. Criteria considered were age, assistive device use, cognition, and fall history. The IMSFPRN reached consensus agreement to recommend that participants of all ages with varying levels of cognitive ability who are able to ambulate with or without assistance and who have a history of falling should be included in their future falls-prevention trials.
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 | 0.054 | 0.146 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.015 | 0.006 |
| Insufficient payload (model declined to judge) | 0.029 | 0.011 |
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".