Repeated episodes of physical training induced hypoxia, may be associated with improved exercise tolerance in covid-19
Bibliographic record
Abstract
Background: Repeated episodes of hypoxia by coronary artery ligation, precondition the myocardium to adapt against ischemic cardiac damage and arrhythmias. This case aims to highlight the role of hypoxia induced adaptation in exercise tolerance. Case and methods: A male physician aged 77 years, presented with COVID-19 on April 17, 2021. Acute phase COVID-19 pneumonia, and lung fibrosis were diagnosed by high resolution computerized tomography and chronic hypoxia, measured by oximeter (SpO2 saturation between 91%-93%). Regular physical training in the form of slow jogging, morning, and evening, was advised twice daily. Results: Treatment with physical training was associated with improved SpO2 saturation during exercise, from 83-84% to 89-91%. There was a significant increase in oxygen saturation during rest after treatment with physical training for two weeks. It is possible that repeated episodes of hypoxia during physical training, may have induced molecular adaptations in the heart and lungs, leading to increased exercise tolerance with increase in SpO2 saturation. Conclusions: Regular physical training in the form of jogging may be associated with improvement in exercise tolerance without causing hypoxia. There is no other study in humans, to our knowledge, that has examined the role of physical training induced hypoxia to achieve myocardial adaptations, characterized with improved SpO2 saturation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".