Identification of risk factors for falls in people with multiple sclerosis: a systematic review of prospective studies
Bibliographic record
Abstract
ABSTRACT The objective of this study was to identify demographic, clinical, and instrumental variables associated with falls in people with multiple sclerosis (MS), via systematic review, based on prospective data. The search was conducted in these databases: Medline, Web of Science, Bireme e CINAHL via a search strategy that combined the descriptors “multiple sclerosis”, “falls”, “accidental falls”, “fall risk”, “postural control” and “balance”, followed by manual search. Eligibility criteria were prospective cohort studies with a minimum 3-month follow-up of falls that assessed the association of a demographic, clinical or instrumental variable in relation to a higher fall risk in people with MS. The modified Newcastle-Ottawa Quality Assessment Scale was used for study quality assessment. A total of 357 studies were identified, of which 12 were included in the systematic review and 1,270 patients were included. In this study, 740 (58.26%) patients were classified as fallers, 396 (31.18%) patients had recurrent falls (2≥falls within the stipulated period) and 530 patients (41.74%) were classified as non-fallers. Except for spasticity and dual task cost in gait speed, all investigated variables showed conflicting results regarding their association with a higher fall risk. More studies with clinical homogeneity phenotypes of MS individuals and using validated assessment instruments are necessary to establish a robust association of other clinical, instrumental, and demographic variables with a higher fall risk.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".