The effect of intense physical exercise on von Willebrand factor and on menstrual blood loss in women with von Willebrand Disease
Bibliographic record
Abstract
The principal objective of this study was to examine the effect of intense physical exercise on menstrual blood loss in women with Type I von Willebrand disease (vWD). First, we investigated the effect of exercise on the level of the von Willebrand protein (which is deficient in the disease) in a pre-test post-test quasi-experiment conducted on a single group of 40 healthy adult pre-menopausal female volunteers recruited from Sainte-Justine Hospital in Montreal between October and December, 2001. The von Willebrand protein (vWF:Ag), coagulation Factor VIII (FVIII:C), bleeding time (BT), coagulation time (aPTT) and several markers of exercise intensity (sweat sodium, lactate, noradrenaline, adrenaline) were measured before and after a standardized exercise session. The significance of the change in these values with exercise was assessed using a paired Student's t-test. The exercise markers were explored as potential predictors of the exercise-related change in vWF:Ag using multiple linear regression. Results showed that there was an absolute mean increase of 0.30 (95% confidence interval (95% CI) 0.23-0.37) and 0.60 (95% CI 0.44-0.76) in vWF:Ag and FVIILC, respectively, and a significant shortening of the BT and aPTT due to exercise. The change in the sweat sodium collected from patches applied to the forearm during exercise (a marker of exercise intensity) was found to be a significant predictor of the change in vWF:Ag induced by exercise (regression coefficient = 0.05 (95% CI 0.01-0.09). Changes of 1, 5 and 10 units in sodium were associated with average changes of 0.05, 0.26 and 0.52, respectively, in vWF:Ag from baseline (mean 0.83 U/ml). Next, we set out to assess the feasibility and acceptability of a 4-period randomized crossover trial in order to evaluate the effectiveness of exercise in reducing the menstrual blood flow in women with Type I vWD. The methods and protocol of this feasibility study are outlined in this thesis and issues related to patient recruitment, compliance and withdrawals are addressed. The strengths and pitfalls of the crossover design feasibility study are discussed and revisions for the definitive trial are recommended.
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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 | 0.003 |
| 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".