Premature Ovarian Failure Related to SARS-CoV-2 Infection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is known to have a wide spectrum of effects on the respiratory, cardiac, neurological, hematopoietic, gastrointestinal, ocular and urological systems, but there is very little information on its effects on the human ovary. Our aims are to describe a unique case that developed amenorrhea during and after SARS-CoV-2 infection and to push researchers to do more researches to understand the effects of SARS-CoV-2 infection on the ovaries. A 27-year-old female patient presented with amenorrhea. She had fever on the second day of the menstrual cycle, and her cycle had been interrupted on the same day. The patient had a sub-febrile temperature, myalgia, fatigue, sweating, loss of appetite, and mild sleep disorder. Based on clinical, laboratory, and reverse transcription polymerase chain reaction (RT-PCR) data of a nasopharyngeal swab sample, she had a positive result for SARS-CoV-2 infection. Till now there are limited publications on the effect of SARS-CoV-2 infection on the ovaries. In particular, the potential adverse effects of SARS-CoV-2 infection on fertility are unclear. Coronavirus disease 2019 (COVID-19) patients need to be followed up for a long time, and clinicians need to pay attention to menstrual disturbances, especially in young female patients. More evidence, through both epidemiologic and clinical studies, as well as long-term follow-up studies, is needed to understand the impact of this infection on the human ovary, especially in reproductive-aged women.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it