Bibliographic record
Abstract
Endometriosis debilitates many women in the U.S. and around the world which is characterized by lesions either localized on the uterus or attached to other organs. These lesions act as endometrial tissue which means that during the monthly menstrual cycle, this tissue sheds which results in blood being stuck in body cavities. The only definitive way to diagnose endometriosis is to go through a laparoscopic procedure which is invasive and expensive. Patients may avoid their endometriosis and rely on pain medications to get relief from their symptoms. Biomarkers can be the next method of diagnosis which is noninvasive. Biomarkers can be taken from proteins during angiogenesis, blood, urine, saliva, and genomics. Blood and saliva have a common biomarker of miRNA. CA-125 in the blood is the most common biomarker used to detect endometriosis but it isn’t always accurate. Saliva can remain stable without extra precautions which makes it an ideal method of gaining and testing biomarkers. However, a panel of biomarkers may also be beneficial. Additionally, there may be specific genes in DNA that can show that a patient has endometriosis. An efficient, non-invasive diagnosis method is needed to reduce the amount of time taken to get a diagnosis and get treatment for symptoms closer to the onset of the disease.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".