Treatment of menopausal vasomotor symptoms and sensory issues in a 53-year-old autistic woman – a case report
Bibliographic record
Abstract
Considered a taboo topic in many societies, menopause in autistic women is still under-recognised, and little research addresses its impact. Existing studies suggest that this often-difficult transition period is associated with numerous unmet health needs and a frustrating lack of knowledge and support from healthcare professionals. Menopausal vasomotor symptoms, the hallmark of menopause, can intensify and worsen sensory sensitivities in many autistic women. We present the case of a 53-year-old Caucasian autistic woman without intellectual disability or other current psychiatric comorbidities. The patient underwent gynaecologic and hormonal lab testing, and was introduced to hormone replacement therapy to ameliorate symptoms of oestradiol and progesterone deficiency. Menopausal vasomotor symptoms experienced by the patient were monitored over a period of seven months following the implementation of hormone replacement therapy and non-pharmacologic approaches. Hormone replacement therapy decreased menopausal vasomotor symptoms, but not all her initial complains. Strong sensory issues were linked to more severe menopausal vasomotor symptoms. This case highlights the need for further research and the development of the global Extension for Community Health Outcomes (ECHO) Autism® model to advance medical care offered to autistic women during menopause.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".