Determining Mean Heart Rate At Symptomatic Threshold In Post-Concussion Syndrome
Bibliographic record
Abstract
Aerobic exercise protocols are promising for rehabilitating individuals with post-concussion syndrome. However, these protocols usually include triggering/exacerbating symptoms to determine the starting intensity and to track progress and adjust exercise intensity. Unfortunately, triggering/exacerbating symptoms can impede recovery and deter adherence. PURPOSE: We sought to provide a less traumatic therapeutic approach by establishing the threshold at which symptoms are triggered/exacerbated. This will enable to avoid exercise-induced symptoms and allow personalising the aerobic exercise protocol by providing a sub-threshold starting intensity. METHOD: Forty-two concussed individuals (24 yrs., ± 9.8) reporting persisting symptoms at rest (M=149.1 ± 233.7; F=63.7 ± 128.2 days) completed a graded exercise test (GXT) on a stationary ergocycle until symptoms were triggered/exacerbated. RESULTS: Mean resting heart rate was 77.8 bpm (± 10.1) for men and 71.1 bpm (± 10.8) for women. Symptoms were triggered/exacerbated at 62.9 % (± 8.2) of maximal theoretical heart rate (MTHR) for men and 58.8 % (± 8.1) of MTHR for women. For both men and women, there is a significant correlation (M=0.451; F=0.762) between resting heart and symptom threshold (% of MTHR). Further, for men, number of days since injury is significantly correlated with the symptom score (0.712) and resting heart rate (0.467). There was no other significant correlation for either group between symptomatic threshold and age at injury, number of symptoms at rest, or number of injuries (p > 0.05). CONCLUSION: These results could help define a more adapted starting intensity for symptomatic individuals. They could also contribute to the development of an algorithm for a sub-maximal progressive aerobic exercise therapy, which would take into account gender differences.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".