A Comparative Study of Objective Outcome Measures Used in Clinical Trials of Freezing of Gait 
Bibliographic record
Abstract
Abstract Background: Freezing of gait (FOG) is notoriously difficult to quantify, leading to multiple metrics utilized as outcomes for clinical trials. The instrumented timed up a go and the many parameters that can be derived from it are commonly used as objective markers of gait severity in FOG trials, however it is unknown if they represent FOG severity. Objective: To determine the specificity and responsiveness of objective surrogate markers of FOG severity commonly utilized in FOG studies. Methods: Markers compared included: velocity, step/stride length, step/stride length variability, TUG, and turn duration. Data was collected in four conditions (ON and OFF dopaminergic drugs, with and without a dual task). Unified Parkinson’s Disease rating scale (UPDRS) was administered in the ON and OFF states. Results: 33 subjects were recruited (17 PD subjects without FOG (PD-control), and 16 subjects with PD and dopa-responsive FOG PD-FOG). The UPDRS motor scores were: 24.9 for the PD-control group in the ON state, 24.8 for the FOG group in the ON state, 42.4 for the FOG group in the OFF state. Significant mean differences between the ON and OFF conditions were observed with all surrogate markers (p<0.01). However, only dual task turn duration and step variability showed trends toward significance when comparing PD-control and ON-FOG (p=0.08). Test-retest reliability was high (ICC >0.90) for all markers except standard deviations. Step length variability was the only marker to show an area under the ROC curve analysis >0.70 comparing ON-FOG vs. PD-control. Conclusions: Multiple candidate surrogate markers for FOG severity showed responsiveness to levodopa challenge, however, most were not specific for FOG severity.
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 | 0.024 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".