Clinical perspectives on the menstrual pictogram for the assessment of heavy menstrual bleeding
Bibliographic record
Abstract
Heavy menstrual bleeding (HMB) has an estimated prevalence of 18-32% but is known to be under-reported due to poor recognition and estimation of menstrual blood loss (MBL). HMB can negatively impact quality of life, affecting social interactions, work productivity and sexual life. Abnormal menstrual bleeding may have an underlying structural or systemic cause, such as endometrial and myometrial disorders; however, for some, there is no identified pathological cause. Several methods are available for assessing MBL, including the alkaline hematin (AH) method and the menstrual pictogram (MP). The AH method is considered to be the most accurate way to monitor MBL; however, it is associated with inconvenience and expense, therefore limiting its value outside of research. The MP requires the user to select an icon from a chart that reflects the appearance of a used sanitary product; the icon is associated with a blood volume that can be used to determine MBL. Validation studies have demonstrated that the results of the MP and AH method are well correlated, showing that the MP can measure MBL with sufficient accuracy. Additionally, the MP is more convenient for users, less expensive than the AH method, may be used in regions where the AH method is unavailable and may also be used as part of a digital application. Overall, the MP offers a convenient approach to monitor MBL both in research and clinical practice settings.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".