Quality of life in adolescents with heavy menstrual bleeding: Validation of the Adolescent Menstrual Bleeding Questionnaire (aMBQ)
Bibliographic record
Abstract
Background/Objectives Heavy menstrual bleeding (HMB) affects 34% to 37% of adolescent girls. The Menstrual Bleeding Questionnaire (MBQ) is a validated measure of menstrual bleeding–specific health-related quality of life (HRQoL) for women aged ≥18 years. No similar measure existed for adolescents with HMB. Patients/Methods HMB was defined by the Pictorial Bleeding Assessment Chart (PBAC) score ≥100. In Phase 1, a focus group of adolescents with HMB adapted the MBQ, to generate the Adolescent MBQ (aMBQ). In phase 2, participants with and without HMB were recruited from clinics and self-referral. Each participant completed 3 questionnaires (aMBQ, Pediatric Quality of Life module [PedsQL]©, PBAC) at two time points. Validity of the aMBQ was assessed by Pearson’s correlation with the PedsQL©. Reliability was calculated using intra-class correlation (ICC) in those without HMB. The receiver operating characteristic curve assessed the aMBQ’s ability to identify those with HMB. Results Phase 1 included five girls with a mean age of 17.1 (13-18) years. The aMBQ was adapted from the MBQ by substituting four words/phrases that altered 8 of the 20 questions and by adding 1 new question. The 21-item aMBQ has a score range of 0 to 77 (77 = worst HRQoL). Phase 2 included 52 participants: 20 with and 32 without HMB, with a mean age of 14.8 (11-17) years. The validity of the aMBQ was confirmed by a moderate correlation with PedsQL© (r = −0.63; P < .001). Test-retest reliability was substantial (ICC = 0.73; P = .04). An aMBQ score of >30 identified those with HMB with excellent discrimination (area under the curve = 0.82; sensitivity, 70.0%; specificity, 84.4%). Conclusions The aMBQ is a valid and reliable tool to assess HRQoL in adolescents with HMB.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".