Predicting the Effectiveness of Physical Therapy in Hockey Players after Cerebral Concussion
Bibliographic record
Abstract
The aim is to develop a prognostic model of rehabilitation for the restoration of motor and cognitive functions in hockey players after a cerebral concussion. The research covered 80 hockey players aged from 17 to 51. Research methods: generalization of scientific and methodological literature, clinical, instrumental, functional methods, and methods of mathematical statistics. The greater effectiveness of the developed physical therapy program in comparison with the standard one has been proved according to the following indicators: limitation of life-sustaining activities by 26.0 ± 2.1%, tone of the autonomic nervous system according to the Kerdo index by 9.9 ± 0.8%, heart rate variability according to statistical indicators of standard deviation of cardiac intervals and variation range by 4.6 ± 0.3% and 28.2 ± 3.5%, respectively, according to the index of autonomic balance by 33.7 ± 4.9%, decrease in the stress index of regulatory systems by 22.5 ± 4.6%, decrease in the time of the test performance with tandem walking and cognitive task by 20.1 ± 1.6%, increase in the score on the Montreal Cognitive Assessment Scale by 12.4 ± 2.0%. According to prognostic model, the most significant factors aggravating the prognosis are the level of headache according to the visual analogue pain scale (regression coefficient B = -0.12), the number of repeated cerebral concussions (B = -1.02); prognostically favorable factors are the general level of cognitive functions (B = 0.03), a lower level of sympathicotonia according to the autonomic balance index (B = 0.03) and the Kerdo index (B = -0.08). The developed model provides results within 20.0% of the existing actual values, which indicates satisfactory and effective work (determination coefficient of 54.0 %, p < 0.05).
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".