Prevention and outcome measurement strategies to reduce the future impact of back pain in handball
Bibliographic record
Abstract
From a physical therapist's perspective, one of the best ways to prevent back pain is to try to identify variables determined to be predisposing factors and to control certain somatic-related factors in junior players.To take steps in this direction, in close correlation with theoretical arguments, we proposed as prevention tools some specific outcome measurement strategies to control and verify over time certain factors that predispose to back pain episodes, such as low levels of the trunk muscular endurance and coordination or lateral muscular asymmetries.The existence of muscle asymmetries was identified by bioimpedance performed with the Tanita MC 780 MA analyzer and trunk muscle endurance and coordination levels were checked with the McGill trunk muscle endurance test battery.Junior players showed poor trunk muscle strength and an imbalance between the three main muscle groups and some lateral muscle mass asymmetries between dominant and non-dominant body parts.Approaching and presenting this strategic vision for the prevention of back pain in handball players can provide professionals in the field with information to guide, evaluate, verify and implement a future individualized prevention program.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".