Comparison of Rmi (Risk Malignancy Index) and Simple Rules Risk Model In Evaluation of Adnexal Masses Taking Histopathology As Gold Standard
Bibliographic record
Abstract
Objective: To compare ultrasound grounded International Ovarian Tumor Analysis (IOTA) prediction models, specifically, the ADNEX models, the Simple Rules (SRs) and the Risk of Malignancy Index (RMI), for the adnexal masses’ diagnosis before any surgical intervention Study Design: Cross-sectional analytical study Place and Duration of Study: Obstetrics and Gynecology Department. Pakistan Emirates Military Hospital, Rawalpindi, Pakistan from Aug 2019 to Jun 2020. Methodology: Five hundred and twenty-four patients took part in this cross-sectional Analytical study. All findings on ultrasound were evaluated and prognostic models were used. Histopathology findings were used as standard for comparison. Diagnostic performances of the prediction models were assessed by estimating sensitivities, ROC curves, negative predictive values and positive predictive values, specificities, diagnostic odds ratios and negative and positive likelihood ratios. Results: The ROC under curves (AUC) areas for ADNEX models were 0.94 (0.92-0.96, 95% CI) with CA125 and 0.94 (0.91-0.96, 95% CI) without CA-125 for RMI I-III It was expressively advanced than AUC: 0.83 (CI95%, 0.80 to 0.86), 0.82 (CI95%, 0.78 to 0.86 and 0.87 (CI95% 0.83 to 0.90) (all p <0.0001). The CA-125 had a cut-off point of 10% in ADNEX model had the maximum precision (CI: 95%, 0.87 to 0.97) equated to other models. The SR model achieved 0.93 (95% CI 0.86 to 0.97) sensitivity and 0.86 (95% CI 0.82 to 0.89) specificity when not diagnosed was classified as definite (11.7%) malignant. Conclusions: ADNEX and Simple rules risk models were excellent for characterizing adnexal masses better than RMI in Pakistani patients.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".