Clinical risk factors for ovarian torsion
Bibliographic record
Abstract
Ovarian torsion is a relatively common gynaecological emergency, usually presenting as acute lower abdominal pain. The underlying pathophysiology involves torsion of the ovarian tissue on its pedicle leading to reduced venous return, stromal oedema, internal haemorrhage and infarction with the subsequent sequelae. It is not clear from looking at the literature which factors are responsible for the development of ovarian torsion and what are the odds of a particular clinical feature in determining the likelihood of developing ovarian torsion. In order to assess the likelihood of a particular clinical feature to be a risk factor for ovarian torsion, we studied the prevalence of each presenting clinical feature in the background population of women, for instance, looking at ovarian cysts and compared this with the odds of the feature occurring in the affected population of torsion patients. Thus we compared the odds of various clinical variables in ovarian torsion patients against the odds of the same feature occurring in the background population of women. Ovarian cysts are three times more common in ovarian torsion cohorts than in the general population. Evidence suggests that ovarian cysts are very common in the asymptomatic pregnant cohorts; however, they spontaneously resolve as the pregnancy progresses. Pregnancy is a risk factor for torsion (odds ratio: 18:1); however, it remains an uncommon event (0.167%). Tubal sterilisation practices vary according to geographical location and over chronology of the published literature. After considering the extremes of variation in tubal sterilisation practices, the risk of torsion increases by at least 8-fold following surgery. Hysterectomy with ovarian conservation is not a risk factor of torsion.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".