PO-0981: Results from the Image Biomarker Standardisation Initiative
Bibliographic record
Abstract
ResultsThe PT model for LRC comprised 3 radiomic features.The mixed model used 4 PT radiomic features (from LC prediction), as a preliminary prediction, combined with 4 LN radiomic features.Both models were significantly associated with LRC in the training and validation cohorts (CI_training_PT = 0.71, CI_validaton_PT = 0.70, CI_training_mixed = 0.80, CI_validaton_mixed = 0.74).The mixed model showed significantly higher performance than the PT model for prediction of LRC (p < 0.01).In the combination of PT radiomics and clinical nodal status (TNM) for prediction of LRC, the nodal status was not a significant predictor. ConclusionThis study shows for the first time that modeling using combined radiomics of the primary tumor and involved lymph nodes improves prediction of the composite endpoint LRC in comparison to primary tumor radiomics only.
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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 | 0.025 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".