Bibliographic record
Abstract
Endometriosis is a disease that affects approximately 5.5 million girls and women in their reproductive years in the United States and Canada. During the menstrual cycle, the endometrial lining of the uterus thickens in preparation to receive a fertilized egg. If fertilization does not occur, this lining sloughs off during menstruation. Endometrial tissue can migrate out of the fallopian tubes and grow outside of the uterus as endometrial implants. This can result in severe pain. Endometriosis is very difficult to detect because most women become accustomed to painful menstrual cycles at an early age. The disease often will go undiagnosed because even with extensive endometriosis it is possible to have minimal symptoms or none at all. Physicians have few diagnostic tools to detect the scars and growths of endometriosis. The only way to confirm the diagnosis and stage of endometriosis is by laparoscopy. Treatment options include hormone therapy and surgery. In the past, the most successful treatment was open laparotomy with excision of the endometrial implants. This article discusses the use of the ultrasonic scalpel to resect endometrial implants through the laparoscope. The patient's perioperative course also is discussed.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".