iRECIST and atypical patterns of response to immuno-oncology drugs
Bibliographic record
Abstract
With the advent of immunotherapy as one of the keystones of the treatment of our patients with cancer, a number of atypical patterns of response to these agents has been identified. These include pseudoprogression, where the tumor initially shows objective growth before decreasing in size, and hyperprogression, hypothesized to be a drug-induced acceleration of the tumor burden. Despite it being >10 years since the first immune-oncology drug was approved, neither the biology behind these paradoxical responses has been well understood, nor their incidence, identification criteria, predictive biomarkers, or clinical impact have been fully described. Immune-based Response Evaluation Criteria in Solid Tumors (iRECIST) guidelines have been published as a revision to the RECIST V.1.1 criteria for use in trials of immunotherapeutics, and the iRECIST subcommittee (of the RECIST Working Group) is working on elucidating these aspects, with data sharing a current major challenge to move forward with this unmet need in immuno-oncology.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".