Measuring the Utility of the Adolescent Menstrual Bleeding Questionnaire (aMBQ) in a Culturally Diverse Adolescent Population
Bibliographic record
Abstract
Introduction Heavy menstrual bleeding (HMB) is common in menstruating adolescents. Accurately identifying HMB can be clinically challenging due to difficulties in measuring blood loss. The Pictorial Bleeding Assessment Chart (PBAC) assesses menstrual blood loss via sanitary product saturation but does not capture the full patient experience. Given the significant emotional, physical, and social impacts of HMB, assessing its effect on health-related quality of life (HRQoL) is essential for comprehensive evaluation and management. The Adolescent Menstrual Bleeding Questionnaire (aMBQ), adapted from the MBQ and validated by Pike et al. (2021) with 75 participants in Nova Scotia, Canada, assesses QoL related to menstrual bleeding in adolescents ≤18. This study aims to measure the utility of the aMBQ in a larger, more culturally diverse adolescent population. Methods Participants were recruited from primary care clinics at Nationwide Children's Hospital (Columbus, OH)- which serves a diverse and predominantly underserved population. Those with a prior diagnosis of a bleeding disorder or chronic illness associated with iron deficiency were excluded. HMB was defined as PBAC score ≥100. The aMBQ is a 21-item instrument with scores ranging from 0-77; higher scores indicate worse HRQoL. Statistical analyses were conducted using Wilcoxon rank sum test, Fisher's exact test, two-sample t-tests, and two-way ANOVA test, with significance set at p<0.05. The aMBQ's ability to distinguish between participants with and without HMB was evaluated using sensitivity, specificity, and the area under the curve (AUC) and corresponding 95% CI from receiver operating characteristic (ROC) analysis. Results A total of 320 participants completed the aMBQ and PBAC. Median PBAC score was 94 (IQR 50 -186), and median aMBQ score was 17 (IQR 11-25). 65% of participants identified as Black, 18% White, 17% other races, and 8% as Hispanic. Mean age was 14.8 years (SD 2.1). 46.9% of participants (n=150) had HMB (PBAC ≥100). Median aMBQ score for those with HMB was higher than those without HMB (24, IQR 17-30 vs. 13, IQR 9-17; p < .001), indicating decreased HRQoL, consistent with the original study. Median age of adolescents with HMB was also significantly higher (15 vs 14 yrs, p=0.002), as was the median time since menarche (4 vs 2 yrs, p < .001). No significant difference in race/ethnicity was observed between the groups. Age at menarche did not significantly differ between those with and without HMB, consistent with the original study. Mean (±SD) aMBQ score for participants with HMB was significantly higher in the original study (29.8, ± 8.5) compared to this study (24.2, ± 9.3; p=0.0105). For those without HMB, the mean aMBQ score in the original study was 19.6 (± 7.6) versus 14.3 (± 7.6) in this study (p=0.0026). The aMBQ had an AUC of 0.81 (95% CI: 0.76-0.86) for HMB (PBAC ≥100). An optimal aMBQ cutoff score of 16.5 showed 79% sensitivity and 71% specificity, compared to the original study's cutoff of ≥30, which had 70% sensitivity and 84% specificity. Personal communication with Pike et al. suggests that a cutoff of 16 in the original study yields similar sensitivity (78%) and specificity (68%). In a sub-analysis with PBAC ≥150 denoting HMB, the mean aMBQ score for those with HMB was 26.3 (± 9.4) vs 15.4 (± 7.8) for those without (p < .001), with an AUC of 0.82 (95% CI: 0.77-0.87) and a cutoff score of 17.5, 69% sensitivity, and 82% specificity. Conclusion This study evaluated the aMBQ in a culturally diverse adolescent cohort, supporting its discriminatory power and utility in assessing HRQoL in menstruating adolescents. The substantial impact of HMB on adolescents' HRQoL, as demonstrated by higher aMBQ scores, highlights the importance of including quality of life measures in HMB evaluation. Higher reported HRQoL compared to the original study may be due to cultural differences in perception and reporting, or a higher prevalence of chronic conditions. Menstrual issues may seem less significant amidst other life challenges and adolescents in underserved populations may have developed coping mechanisms to manage HMB. Future research should further examine the aMBQ across various demographic groups and cultures to enhance its applicability. Reference Pike M, et al. Quality of life in adolescents with heavy menstrual bleeding: Validation of the Adolescent Menstrual Bleeding Questionnaire. Res Pract Thromb Haemost. 2021 Nov 8;5(7):e12615.
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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) | 0.000 | 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) | 0.000 | 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".