Additive toxicity arising from combined use of immune checkpoint inhibitors and tyrosine kinase inhibitors in patients with renal or endometrial carcinoma: Protocol for a rapid systematic review
Bibliographic record
Abstract
The combined use of immune checkpoint inhibitors and tyrosine kinase inhibitors (ICI/TKI) is an effective treatment strategy for some cancers. A better understanding of the potential additive toxicity for ICI/TKI combinations is needed to inform patient and provider treatment decisions. We aim to evaluate the safety of ICI/TKI combinations for individuals with renal cell or endometrial carcinoma. This rapid systematic review (SR) protocol follows PRISMA guidelines. A systematic search will be designed, peer reviewed and executed by experienced information specialists (Cochrane Central, MEDLINE, Embase) to identify published SRs and primary studies published since the most recent SR search. Randomized, quasi- or non-randomized controlled trials and comparative cohort studies are eligible if they compare ICI/TKI combinations to monotherapy or standard of care in participants with renal cell or endometrial carcinoma. The primary outcome is grade ≥ 3 treatment-related adverse-effects. Studies will be screened, selected, extracted and assessed for risk of bias by a single reviewer and checked completely by a second. Where feasible and appropriate, we will pool studies separately by design and indication using meta-analysis and test robustness of effects using prespecified subgroup and sensitivity analyses. Results will be summarized descriptively and presented in tables and figures. (PROSPERO ID: CRD42023416388).•This will be a comprehensive systematic review of the additive toxicity arising from the combined use of ICI/TKIs in patients with renal-cell or endometrial carcinoma.•We will consider treatment-related, treatment-emergent adverse events (Grade 3 or higher).•Identified safety profile may be used to inform patient or provider treatment decisions.
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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 | 0.073 | 0.105 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.018 | 0.022 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.079 | 0.011 |
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".