Premature Cardiovascular Misdiagnosis of Senior Endurance-Trained Athletes
Bibliographic record
Abstract
A mature Caucasian patient, an endurance-trained triathlete (age group), had a routine ECG. The patient was immediately referred to Emergency based on supposed ECG abnormalities indicating a heart attack. This diagnosis was quickly dismissed based on no symptoms, heart rate of 50 BPM, athletic status, excellent health, and no prior cardiovascular problems. The patient had a history of severe white coat hypertension and underwent a further stress test and echocardiogram. The stress test showed exaggerated systolic blood pressures (over 225 mmHg) and high in-clinic basal blood pressures (160/90 mmHg), and the patient was diagnosed as hypertensive with exercise blood pressure close to stroke territory. He was told to stop racing, reduce training, and was prescribed antihypertensive drugs (which he did not take). Subsequent at-home 24 h (values close to 120/80 mmHg) and stress blood pressure measurements reversed that decision when considered in combination with an excellent echocardiogram result. The literature clearly describes endurance-trained athletes with systolic pressures over 225 mmHg Hg as being conditioned with no pathological aspects. Endurance-trained athletes should be examined as special cases in the field of cardiovascular medicine as trained physiological responses often present as cardiac abnormalities, and misdiagnosis can inappropriately change the athlete’s life.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".