Exploring Self-Reported Symptoms for Developing and Evaluating Digital Symptom Checkers for Polycystic Ovarian Syndrome, Endometriosis, and Uterine Fibroids: Exploratory Survey Study
Bibliographic record
Abstract
BACKGROUND: Reproductive health conditions such as polycystic ovary syndrome (PCOS), endometriosis, and uterine fibroids pose a significant burden to people who menstruate, health care systems, and economies. Despite clinical guidelines for each condition, prolonged delays in diagnosis are commonplace, resulting in an increase to health care costs and risk of health complications. Symptom checker apps have the potential to significantly reduce time to diagnosis by providing users with health information and tools to better understand their symptoms. OBJECTIVE: This study aims to study the prevalence and predictive importance of self-reported symptoms of PCOS, endometriosis, and uterine fibroids, and to explore the efficacy of 3 symptom checkers (developed by Flo Health UK Limited) that use self-reported symptoms when screening for each condition. METHODS: Flo's symptom checkers were transcribed into separate web-based surveys for PCOS, endometriosis, and uterine fibroids, asking respondents their diagnostic history for each condition. Participants were aged 18 years or older, female, and living in the United States. Participants either had a confirmed diagnosis (condition-positive) and reported symptoms retrospectively as experienced at the time of diagnosis, or they had not been examined for the condition (condition-negative) and reported their current symptoms as experienced at the time of surveying. Symptom prevalence was calculated for each condition based on the surveys. Least absolute shrinkage and selection operator regression was used to identify key symptoms for predicting each condition. Participants' symptoms were processed by Flo's 3 single-condition symptom checkers, and accuracy was assessed by comparing the symptom checker output with the participant's condition designation. RESULTS: A total of 1317 participants were included with 418, 476, and 423 in the PCOS, endometriosis, and uterine fibroids groups, respectively. The most prevalent symptoms for PCOS were fatigue (92%), feeling anxious (87%), BMI over 25 (84%); for endometriosis: very regular lower abdominal pain (89%), fatigue (85%), and referred lower back pain (80%); for uterine fibroids: fatigue (76%), bloating (69%), and changing sanitary protection often (68%). Symptoms of anovulation and amenorrhea (long periods, irregular cycles, and absent periods), and hyperandrogenism (excess hair on chin and abdomen, scalp hair loss, and BMI over 25) were identified as the most predictive symptoms for PCOS, while symptoms related to abdominal pain and the effect pain has on life, bleeding, and fertility complications were among the most predictive symptoms for both endometriosis and uterine fibroids. Symptom checker accuracy was 78%, 73%, and 75% for PCOS, endometriosis, and uterine fibroids, respectively. CONCLUSIONS: This exploratory study characterizes self-reported symptomatology and identifies the key predictive symptoms for 3 reproductive conditions. The Flo symptom checkers were evaluated using real, self-reported symptoms and demonstrated high levels of accuracy.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".