Let's Talk Period! Preliminary results of an online bleeding awareness knowledge translation project and bleeding assessment tool promoted on social media
Bibliographic record
Abstract
INTRODUCTION: Undiagnosed bleeding disorders are common and can pose significant health risks, especially for women. Recently, a self-administered bleeding assessment tool (Self-BAT) was validated in von Willebrand disease. AIM: To increase awareness of undiagnosed bleeding disorders through the use of an informational website (http://letstalkperiod.ca) targeted at women in their reproductive years. METHODS: The Let's Talk Period website was built in consultation with a medical communications company and focus groups of women, with the aim of clearly presenting key messages around menstrual bleeding. The website was promoted through social media and local and national interviews. Upon completion of the online Self-BAT available at http://letstalkperiod.ca, the result is displayed to the user along with a recommendation to seek medical attention if the score is abnormal. RESULTS: During the initial 3-month period, there were 5158 page views from 64 countries. A total of 489 individuals, 95% female, completed the online Self-BAT. The mean Self-BAT score was 6, range 0-44. Abnormal Self-BAT scores were reported in 45% of the respondents, of whom 96% were female. The most commonly reported bleeding symptoms were menorrhagia (98%) and postpartum haemorrhage (82%). Bleeding symptoms were similar across different geographical areas. CONCLUSION: An online screening tool is an effective method of identifying individuals concerned with abnormal bleeding. A significant portion of the general population report experiencing symptoms of abnormal bleeding. In women, the most frequently reported bleeding symptoms were menorrhagia and postpartum haemorrhage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".