A meta‐analysis of six prospective studies of falling in Parkinson's disease
Bibliographic record
Abstract
Recurrent falls are a disabling feature of Parkinson's disease (PD). We have estimated the incidence of falling over a prospective 3 month follow-up from a large sample size, identified predictors for falling for PD patients repeated this analysis for patients without prior falls, and examined the risk of falling with increasing disease severity. We pooled six prospective studies of falling in PD (n = 473), and examined the predictive power of variables that were common to most studies. The 3-month fall rate was 46% (95% confidence interval: 38-54%). Interestingly, even among subjects without prior falls, this fall rate was 21% (12-35%). The best predictor of falling was two or more falls in the previous year (sensitivity 68%; specificity 81%). The risk of falling rose as UPDRS increased, to about a 60% chance of falling for UPDRS values 25 to 35, but remained at this level thereafter with a tendency to taper off towards later disease stages. These results confirm the high frequency of falling in PD, as almost 50% of patients fell during a short period of only 3 months. The strongest predictor of falling was prior falls in the preceding year, but even subjects without any prior falls had a considerable risk of sustaining future falls. Disease severity was not a good predictor of falls, possibly due to the complex U-shaped relation with falls. Early identification of the very first fall therefore remains difficult, and new prediction methods must be developed.
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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.040 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".