Bibliographic record
Abstract
Ovarian torsion is a rare gynecological emergency that occurs when the ovary spontaneously becomes twisted on itself. If not recognized and treated, it can result in tissue necrosis and loss of the ovary. Ovarian torsion most frequently affects women and people with ovaries in their childbearing years, and if oophorectomy is required from missed diagnosis, this can result in decreased fertility of the individual. Due to the broad differential diagnoses present with abdominal pain, ovarian torsion diagnosis is challenging for clinicians. An integrative review of available data was completed to determine factors that impact the diagnosis of ovarian torsion in adult non-pregnant women who present to the emergency department. A literature search was conducted using databases MEDLINE and CINAHL for articles in English and studies published from 2000-2025. A total of 12 papers were reviewed and data extraction completed to ascertain common factors impacting the diagnosis of ovarian torsion. Methods of diagnosis for ovarian torsion include patient history and clinical exam, bloodwork and urinalysis, and imaging via ultrasound and/or computed tomography. There has been improvement in ovarian salvage rate in the last 20 years, possibly due to increased preference for attempting ovarian salvage versus oophorectomy even if ovaries appear dusky on initial exam. No single physical exam or diagnostic can rule out torsion definitively, and care should not be delayed obtaining an ultrasound if high suspicion for ovarian torsion. The gold standard for diagnosis and treatment of ovarian torsion remains operative exploration, and clinicians should not hesitate to involve obstetrics and gynecology promptly when they suspect a patient has ovarian torsion.,
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 | 0.007 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".