Advancing real-world research in thoracic malignancies: learnings from the international I-O Optimise initiative
Bibliographic record
Abstract
In recent years, the thoracic malignancies treatment landscape has become more complex with the emergence of novel targeted and immunotherapy-based treatments. Although beneficial to patients and physicians, this fast-paced therapeutic evolution has increased the complexity of clinical decision-making and amplified the importance of real-world evidence to support data from randomized controlled trials. The international I-O Optimise initiative was established in 2016 to provide real-world insights into the thoracic malignancies treatment landscape, and has since collaborated with 14 data sources across Europe and Canada, allowing access to data from ~ 500,000 patients with non-small-cell lung cancer, small-cell lung cancer, and malignant pleural mesothelioma. This article reviews pertinent I-O Optimise research, with discussion of the methodological/data-related learnings and expectations for future insights.
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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 | 0.082 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.011 |
| 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".