Exercise transiently increases the density of incipient blood clots in antiplatelet-treated lacunar stroke patients
Bibliographic record
Abstract
INTRODUCTION: Older individuals and, in particular, individuals at risk of recurrent stroke, may be susceptible to thrombosis when participating in exercise, however, this aspect has not been well investigated. METHODS: Clot microstructure and conventional markers of thrombotic risk were determined in twenty lacunar stroke patients and fifteen healthy age-matched controls before, immediately after and 1 h after a bout of moderate intensity cycling exercise. Data were analyzed using a linear mixed model approach. RESULTS: At rest, clot microstructure (1.69 ± 0.07 vs. 1.64 ± 0.05, corresponding to a difference of ~ 50% in normalized clot mass; p = 0.009) and thrombocyte count (73%; p < 0.0001) were higher, and activated partial thromboplastin time was lower (18%; p = 0.0001) in stroke patients compared to age-matched controls. Acute exercise increased thrombogenic markers similarly in the two groups: incipient clot microstructure (1.69 ± 0.07 vs. 1.74 ± 0.05; p = 0.0004 and 1.64 ± 0.05 vs. 1.71 ± 0.04; p < 0.0001, for stroke and controls respectively), plasma fibrinogen (12%; p < 0.0001 and 18%; p < 0.0001, for stroke and controls respectively) and the combined coagulation factors II, VII and X (p = 0.0001 and p < 0.0001, for stroke and controls respectively). CONCLUSION: The results show that exercise transiently increases the risk of blood clot formation in both stroke patients and controls, however, due to the higher baseline thrombogenicity in stroke patients, the post exercise risk of forming blood clots may be higher in this group. TRIAL REGISTRATION: Registered at ClinicalTrials.gov (NCT03635177).
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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 | 0.000 | 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".