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 (PubMed, Medline, Scopus, Cochrane, and CINAHL) from inception until October 1st, 2023. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, the search included patients with OT that had quantified biomarker expression with no age, geographical location, publication date, language, or setting restrictions. Articles were excluded if OT was found incidentally, was based on qualitative analyses, or were not primary research articles. Full texts of 23 selected articles were assessed for risk of bias and quality assurance using a modified Newcastle–Ottawa Scale (NOS) for clinical studies and SYRCLE’s risk of bias tool for the assessment of pre-clinical (animal) studies. A total of 11 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 albumhumin (IMA), serum D-dimer (s-DD), heat shock protein-70 (hsp-70), Pentraxin-3 (PTX3), and c-reactive protein (CRP) each showed the most promise, with p-values for the difference between OT and control groups achieving ≤ 0.001. In studies of humans, the biomarkers ranged from 16.4 to 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-α). 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 | 0.028 | 0.071 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.042 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 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".