Corrigendum: First-in-human phase 1 dose-escalation results with livmoniplimab, an antibody targeting the GARP: TGF-ß1 complex, as monotherapy and in combination with the anti–PD-1 antibody budigalimab in patients with advanced solid tumors
Bibliographic record
Abstract
Corrigendum on: Shimizu, Toshio et al. “First-in-human phase 1 dose-escalation results with livmoniplimab, an antibody targeting the GARP:TGF-ß1 complex, as monotherapy and in combination with the anti-PD-1 antibody budigalimab in patients with advanced solid tumors.” Frontiers in oncology vol. 14 1376551. 29 Oct. 2024, doi:10.3389/fonc.2024.1376551Error in Figure/TableIn the published article, there was an error in Figure 4B as published. The graph included incorrect labeling of the livmoniplimab dose for the patient with the deepest response (corrected from livmoniplimab 100mg to livmoniplimab 1500mg). The corrected Figure 4B and its caption appear below. Percentage change in target lesion sum diameter measurements from baseline over time per investigator assessment in response-evaluable set (efficacy-evaluable patients defined as patients who have received at least 1 dose of study drug and have either had at least 1 postdose tumor assessment or discontinued treatment due to AE, progressive disease, or death); per RECIST v1.1 and iRECIST.] (A): Livmoniplimab monotherapy (Q2W) cohorts (N=22). (B): Livmoniplimab (Q2W) and budigalimab combination therapy cohorts (N=34). → Denotes patients still on treatment. One patient did not have on-study tumor measurement data due to early death. AE, adverse event; iRECIST, modified RECIST v1.1 criteria for immune-based therapeutics; Q2W, once every 2 weeks; RECIST, Response Evaluation Criteria in Solid Tumors. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.124 | 0.085 |
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".