Goalkeeper game: a new assessment instrument in neurology showed higher predictive power than moca for gait performance in people with parkinson’s disease
Bibliographic record
Abstract
Objective To investigate the use of the Goalkeeper Game (GG) to assess gait automaticity decline under dual task conditions in people with Parkinson’s disease (PPD) and compare its predictive power with the one of the MoCA test. Materials and Methods 74 PPD (H&Y stages: 23 in stage 1; 31 in stage 2; 20 in stage 3), without dementia (MoCA cut-off 23), tested in ON period with dopaminergic medication were submitted to single individual cognitive/motor evaluation sessions. The tests applied were: MoCA, GG, dynamic gait index (DGI) task and timed up and go test (TUG) under single and dual-task (DT) conditions. GG test resulted in 9 measures extracted via a statistical model. The predictive power of the GG measures and the MoCA score with respect to gait performance, as assessed by DGI and DT-TUG, were compared. Results The predictive models based on GG measures and MoCA score obtained, respectively, sensitivities of 65% and 56% for DGI scores and 59% and 57% for DT-TUG cost at a 50% specificity. GG application proved to be feasible and aroused more motivation in PPDs than MoCa. Conclusion GG, a friendly and ludic game, was able to reach a good power of gait performance prediction in people at initial and intermediate stages of PD evolution.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".