10.5152/balkanmedj.2012.105
Bibliographic record
Abstract
BACKGROUND: The finger-tapping test is a commonly employed quantitative assessment tool used to measure motor performance in the upper extremities. This task is a complex motion that is affected by external stimuli, mood and health status. The complexity of this task is difficult to explain with a single average intertap-interval value (time difference between successive tappings) which only provides general information and neglects the temporal effects of the aforementioned factors. AIMS: This study evaluated the time course of average intertap-interval values and the patterns of variation in both the right and left hands of right-handed subjects using a computer-based finger-tapping system. STUDY DESIGN: Cross sectional study. METHODS: Thirty eight male individuals aged between 20 and 28 years (Mean±SD = 22.24±1.65) participated in the study. Participants were asked to perform single-finger-tapping test for 10 seconds of test period. Only the results of right-handed (RH) 35 participants were considered in this study. The test records the time of tapping and saves data as the time difference between successive tappings for further analysis. The average number of tappings and the temporal fluctuation patterns of the intertap-intervals were calculated and compared. The variations in the intertap-interval were evaluated with the best curve fit method. RESULTS: An average tapping speed or tapping rate can reliably be defined for a single-finger tapping test by analysing the graphically presented data of the number of tappings within the test period. However, a different presentation of the same data, namely the intertap-interval values, shows temporal variation as the number of tapping increases. Curve fitting applications indicate that the variation has a biphasic nature. CONCLUSION: The measures obtained in this study reflect the complex nature of the finger-tapping task and are suggested to provide reliable information regarding hand performance. Moreover, the equation reflects both the variations in and the general patterns associated with the task.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.695 | 0.614 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".