Correlation between the Length of Ovarian Ligament and Ovarian Torsion: A Prospective Study
Bibliographic record
Abstract
STUDY OBJECTIVE: The study aimed to evaluate whether there is an association between the ovarian ligament length and ovarian torsion. DESIGN: This is a prospective cohort study. Design Classification: II.2. SETTING: The study was conducted in the gynecology department of a university affiliated hospital. INTERVENTION: We measured the length of the ovarian ligaments during laparoscopy. PATIENTS: A total of 56 women were recruited, of which 28 women were operated for ovarian torsion (torsion group) and 28 others for other gynecologic conditions (control group). MEASUREMENT AND MAIN RESULTS: The study found correlations between ovarian ligament length and ovarian torsion. The length of the right (2.2 ± 0.6 cm) and left ovarian ligament (2.3 ± 0.8 cm) in the control patients were similar. Ovarian torsions occurred mainly on the right side (67.9 %). The right ovarian ligament was significantly longer in the torsion group (3.2 ± 0.9 cm) than in the control group (2.2 ± 0.6 cm; p < 0.001). Even after exclusion of patients with ovarian cyst, the ovarian ligament was still significantly longer in the torsion group as compared to the control group (3.2 ± 1.1 vs. 2.2 ± 0.6 cm respectively, p = 0.01). CONCLUSION: Our results suggest that increased length of ovarian ligament might be correlated with the development of ovarian torsion. This could be a basis for ovarian ligament fixation or oophoropexy at the time of conservative surgery for 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".