Are There Non-invasive Biomarker(s) That Would Facilitate the Detection of Ovarian Torsion? A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Ovarian torsion (OT) is a rare gynaecological emergency that requires a prompt diagnosis for optimal patient management. To determine whether there were any biomarkers suitable for the non-invasive detection of OT, two independent reviewers performed systematic searches of five literature databases following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Full-texts of 24 selected articles were assessed for risk of bias and quality assurance using a modified Newcastle-Ottawa Scale (NOS). 15 articles described studies on animals and all described serum biomarkers comparing results between OT versus a sham operation, a control group or readings before and after OT. Ischaemia modified albumin (IMA), serum D-dimer (s-DD), heat shock protein-70 (hsp-70), Pentraxin-3 (PTX-3) and c-reactive protein (CRP) showed the most promise, each with p-values for the difference between groups achieving ≤0.001. In studies of humans, the biomarkers ranged from 16.4-92.3% sensitivity and 77-100% specificity. The most promising biomarkers for the early prediction of OT in patients included s-DD, interleukin-6 (IL-6), IMA and tumour necrosis factor-alpha (TNF-. IL-6 had the highest sensitivity of any biomarker identified in this review and was raised in all 13 patients with proven OT, with values of ≥10.2pg/ml, indicative of a 16 times higher risk of having OT. Signal peptide, CUB domain and EGF like domain containing 1 (SCUBE1) had a high specificity at 93.3%, second only to s-DD and a positive likelihood ratio (LR)>10. IMA was the only other biomarker that also had a positive LR>10, making it a promising diagnostic biomarker. The studies identified by this systematic literature review each analysed small patient groups but IMA, DD and SCUBE1 nevertheless showed promise as serum biomarkers with a pooled LR>10. However, further well-designed studies are needed to identify and evaluate individual markers, or diagnostic panels to help clinicians manage this important, organ-threatening condition.
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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 | 0.021 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.037 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".