Development of adapted RECIST criteria to assess response in lymphoma and their comparison to the International Workshop Criteria
Bibliographic record
Abstract
RECIST (response evaluation criteria in solid tumours) uses a unidimensional approach to tumour measurement and has been widely adopted for assessing the response rate of new therapies in solid tumour clinical trials. For lymphoma, the IWC (International Workshop Criteria), based on bidimensional product assessment, is generally utilised. We adapted RECIST for use in lymphoma and compared responses with the IWC in three Phase II lymphoma trials (n = 115). Measures of agreement estimated the concordance between the adapted RECIST and the IWC response assessments. A Pearson's coefficient estimated the correlation between changes in uni- and bidimensional measurements in a subset of patients (n = 75). All measures of agreement were very high [kappa = 0.86 (95% CI: 0.76 - 0.95), percent agreement 0.93 (95% CI: 0.87 - 0.97), positive agreement 0.90 (95% CI: 0.87 - 0.98), negative agreement 0.92 (95% CI: 0.89 - 0.98)]. Pearson's coefficient was 0.92 (95% CI: 0.87, 0.95). The lymphoma-adapted RECIST is simpler to apply than the IWC and yields near identical response rates. The adapted RECIST should be considered for inclusion into any new draft of the IWC.
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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 | 0.135 | 0.305 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".