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Immunotherapy toxicity in the curative setting.

2025· article· en· W4410802947 on OpenAlexaff
Amal Aljuhani, Neha Pathak, Brooke E. Wilson, Jacqueline Savill, Yael Berner-Wygoda, Hosam Alghanmi, Michelle B. Nadler

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsPrincess Margaret Cancer CentreQueen's UniversityUniversity Health Network
Fundersnot available
KeywordsMedicineToxicityImmunotherapyOncologyInternal medicineCancerPharmacology

Abstract

fetched live from OpenAlex

e14579 Background: Immune checkpoint inhibitors (ICI) improve survival in the curative setting. Understanding the impact, severity, and long-term toxicity is critical given the implication for survivorship and use of healthcare resources. Study objectives were to quantify the incidence and severity of acute and long-term adverse events (AE) and immune-related AE (irAE) in this setting. Secondary objectives include assessment for associations between trial factors with the incidence/severity of AE and to assess AE reporting across studies. Methods: A retrospective review of all FDA-approved solid-tumor, curative treatment regimens including ICI was performed. Included studies were clinical trials, follow-up studies, or post-marketing AE reports. Studies were excluded if they lacked AE data. Data Sources included the FDA drug database and systematic searches in PubMed, Cochrane Library, and Embase using keywords “immunotherapy,” “curative setting,” and “toxicity.” Data was extracted on study type, regimen, population, outcomes, acute and long-term AE. AE were graded using CTCAE. Organ/system-specific classifications were prioritized, and attention was paid to reporting of irAE. SPSS version 22 was used to calculate descriptive statistics and odds ratio. Follow-up duration was evaluated by determining mean and median lengths reported across studies. Results: Overall, 23 manuscripts were identified (14 original trials; 9 follow-up studies), with 5 trials and 3 follow-up studies examining ICIs in combination with other treatments. Common AE included fatigue, nausea, diarrhea, and pruritis. The rates of any-grade AE increased from 80.1% in original studies to 88.3% in follow-up studies; grade 3-5 AE increased from 34.1% to 53.3%. Among studies with both baseline and follow-up data (n = 9), the average difference in AE rates was 8.2%. Only 7 studies specified if the toxicity was considered an irAE, reporting only on hypo/hyperthyroid, colitis, hypophysitis (n = 5 studies), and pneumonitis (n = 3 studies). Hepatitis, pancreatitis, and myositis were only reported on as separate irAE once. Colitis was the most common irAE in follow-up studies (7.2%). The average incidence of immune-related AE for single-agent ICI was 65.3% versus combination therapy 82.7%. In the primary papers, immunotherapy was associated with a higher risk of any grade AE (OR = 2.12) and grade 3-5 AE (OR = 2.16) compared with control groups. Follow-up studies demonstrated continued risk of grade 3-5 AE (OR = 2.77), particularly in nausea, fatigue, and diarrhea. Conclusions: There is a significant burden of AE associated with curative-intent ICI therapy, with increased incidence and severity over time; however, there is limited data on frequency and impact of irAE. Clinicians and trialists should be vigilant in documenting specifically if an irAE has occurred and its respective grade in order to improve understanding of the burden of these issues on the healthcare system.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.031
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0050.006
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.056
GPT teacher head0.440
Teacher spread0.383 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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