Evaluating the impact of the self‐BAT screening tool on patient outcomes: Results of the let's talk period project
Bibliographic record
Abstract
INTRODUCTION: One of the many challenges in diagnosing bleeding disorders is distinguishing between normal and abnormal bleeding symptoms. Letstalkperiod.ca is an educational website that includes an online self-administered bleeding assessment tool (Self-BAT) which is a validated screening tool that enables patients to independently determine their bleeding scores (BS). AIM: The aim of this study was to evaluate patient outcomes for those referred with an abnormal Self-BAT BS compared to those referred without the prior use of the Self-BAT. METHODS: This was a retrospective, observational study. After obtaining REB approval, chart review was performed for patients evaluated for a suspected bleeding disorder in a tertiary care centre between 2016 and 2023. RESULTS: 351 patients (310 female) were identified for inclusion with a mean age of 41 years. Of these patients, 30 were referred for a positive/abnormal Self-BAT BS and the remainder were referred for other reasons. Patients referred for a positive Self-BAT BS required interventions for their bleeding symptoms more often (73.3% vs. 36.7%, p ≤ .001). Though they were not diagnosed with an inherited bleeding disorder more often (6.7% vs. 10.7%, p = .754), patients referred for a positive self-BAT were more likely to be diagnosed with a bleeding disorder when the definition was expanded to include bleeding disorder of unknown cause (56.7% vs. 31.9%, p = .008). CONCLUSION: Results of this study suggest that the Self-BAT at letstalkperiod.ca can be a useful tool for patients and physicians to identify those needing referral to tertiary haematology clinics for evaluation and management of bleeding symptoms.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".