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Record W4324355305 · doi:10.3390/hearts4010004

Premature Cardiovascular Misdiagnosis of Senior Endurance-Trained Athletes

2023· article· en· W4324355305 on OpenAlexaff
John K. Jackson

Bibliographic record

VenueHearts · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBlood pressureMedicineAthletesCardiologyHeart rateInternal medicinePathologicalPhysical therapyWhite coat hypertensionEndurance trainingAmbulatory blood pressure

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.702

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.257
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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