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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".