A design thinking approach to primary ovarian insufficiency
Bibliographic record
Abstract
Most clinicians are not prepared to provide integrated personal care to address all the clinical needs of women with primary ovarian insufficiency. Design thinking is an engineering methodology used to develop and evaluate novel concepts for systems operation. Here we articulate the need for a seamlessly integrated mobile health system to support genomic research as well as patient care. We also review the pathophysiology and management of primary ovarian insufficiency. Molecular understanding regarding the pathogenesis is essential to developing strategies for prevention, earlier diagnosis, and appropriate management of the disorder. The syndrome is a chronic disorder characterized by oligo/amenorrhea and hypergonadotropic hypogonadism before age 40 years. There may be significant morbidity due to: 1) depression and anxiety related to the loss of reproductive hormones and infertility; 2) associated autoimmune adrenal insufficiency or hypothyroidism; and 3) reduced bone mineral density and increased risk of cardiovascular disease related to estrogen deficiency. Approximately 5% to 10% of women with primary ovarian insufficiency conceive and have a child. Women who develop primary ovarian insufficiency related to a premutation in FMR1 are at risk of having a child with fragile X syndrome, the most common cause of inherited intellectual disability. In most cases of spontaneous primary ovarian insufficiency no environmental exposure or genetic mechanism can be identified. As a rare disease, the diagnosis of primary ovarian insufficiency presents special challenges. Connecting patients and community health providers in real time with investigators who have the requisite knowledge and expertise would help solve this dilemma.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".