Pull test performance and correlation with falls risk in Parkinson?s disease
Bibliographic record
Abstract
Postural instability (PI) and falls are major sources of disability in Parkinson's disease (PD). Our objectives were to evaluate the correlation between the pull-test (PT) scores and falls. Patients underwent a standardized data collection including demographic, clinical data, and the UPDRS scores for falls and the PT. Cases with scores >1 for falls were considered frequent fallers. 264 patients were included with mean age 67.6±10 years, mean age of onset 59.1±10.7 years. Comparison between PT scores versus the proportion of frequent fallers and the mean score of the UPDRS for falls showed that for each increase in the PT score, both parameters were significantly worse, with positive linear relationship. For any abnormal PT score, sensibility and negative predictive value were excellent; specificity and positive predictive value improved with worse PT scores. In conclusion, the PT provides important and reliable information regarding PI and the risk of falls in PD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".