Frailty status predicts falls in early Parkinson’s disease [abstract]
Bibliographic record
Abstract
Objective: To establish whether there is an association between frailty and falls in early Parkinson’s (PD). \n \nBackground: PD is a syndrome in which postural control, falls and gait impairments dominate. PD also is present in the context of ageing in which ageing syndromes such as frailty and multi-morbidity coexist. Frailty has been defined as a state where multiple body systems lose their in-built reserves. To date there has been little exploration of falls risk with respect to frailty. \n \nMethod: As part of the Incidence of Cognitive Impairment in Cohorts with Longitudinal Evaluation – Parkinson’s disease – GAIT (ICICLE – GAIT) study, participants were classified as robust, pre-frail or frail according to the electronic frailty index, comprised of 36 health deficits. They were also categorised as fallers on non-fallers, depending on whether they had fallen in the previous 12 months. \n \nResults: Mean age of the 119 participants was 66.9 (±10.5) years, 66.4% were male, with a disease duration 6.3 (±4.7) months, mean MDS UPDRS III score of 25.4 and Montreal Cognitive Assessment of 25.2. 37 (31.1%) were classified as robust, 52 (43.7%) as pre-frail, and 30 (25.2%) as frail. Of the 119, 26 (21.8%) had fallen in the prior 12 months. Those that were frail were more likely to have fallen (50% fallers were frail vs. 17.3% pre-frail and 5.4% robust, X2=20.4, p<0.001). \n \nConclusion: Even at very early disease, a considerable proportion of PD patients are classified as frail. Frailty status was associated with retrospective falls, suggesting that these may form a different falls phenotype which requires a different approach to falls risk reduction. (Also presented at the Parkinson’s UK Research Conference, York, UK, 12th November 2018)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".