OC07.04: Two‐step strategy to preoperatively assess adnexal lesions not classifiable by <scp>EDs</scp>, using <scp>RMI</scp>, <scp>IOTA ADNEX</scp> or simple rules risk model
Bibliographic record
Abstract
An accurate preoperative characterisation of ovarian pathologies is pivotal to optimise patient management and improve the survival rate. We aimed to evaluate the diagnostic performance of the Risk of Malignancy Index (RMI), the IOTA Assessment of Different NEoplasias in the adneXa (ADNEX) model and the Simple Rules risk model (SRrisk) for discriminating between benign and malignant adnexal tumours in masses not classifiable by Easy Descriptors (EDs). In total, 2403 patients were recruited between 2009 and 2012 in IOTA phase 3, an international prospective multicentre study. All patients had at least one adnexal mass and underwent preoperative standardised transvaginal ultrasonography. Histopathology was the gold standard. When EDs could not be applied as a first step, RMI, ADNEX and SRrisk were used. The diagnostic performance was estimated for each diagnostic model used as a second step test in cases not classifiable by EDs, using area under the receiver operating characteristic curve (AUC). Performance was assessed as sensitivity at 80% specificity. Single imputation was used to deal with missing CA125 values. The EDs were applicable in 1017/2403 masses (42%). In the residual group of patients with more ‘difficult’ tumours not classifiable by EDs, ADNEX with CA125 showed the best diagnostic performance when applied as second step (AUC: 0.875), followed by SRrisk model (AUC: 0.863), ADNEX without CA125 (AUC: 0.858) and RMI (AUC: 0.756). At a fixed specificity of 80%, the sensitivities ranged from 92.3 to 93.0% when ADNEX with CA125, ADNEX without CA125 and SRrisk were used as second step tests versus 81.7% when RMI was applied. IOTA ADNEX model and SRrisk model were superior to RMI for correctly discriminating between benign and malignant adnexal masses not classifiable by the EDs.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".