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Record W4410791528 · doi:10.1136/jitc-2024-011184

Clinical factors influencing retreatment with anti-PD-(L)1 therapies after treatment in early-stage cancers: a modified Delphi consensus study

2025· article· en· W4410791528 on OpenAlexaff
Lajos Pusztai, Vernon K. Sondak, Raquel Aguiar‐Ibáñez, Federico Cappuzzo, C. Chouaïd, Chris Elder, Yosuke Hirasawa, Masaru Ishida, R. R. Jones, Seung Hyeun Lee, Ryuichi Mizuno, Masayoshi Nagata, David Okonji, Phillip Parente, Bhavesh Shah, A. Sun, Carmel Spiteri, Andrea Lauer, Amrit Kaliasethi, Carol Kao, Smita Kothari, Jan McKendrick

Bibliographic record

VenueJournal for ImmunoTherapy of Cancer · 2025
Typearticle
Languageen
FieldMedicine
TopicMyasthenia Gravis and Thymoma
Canadian institutionsPrincess Margaret Cancer CentreMerck Canada Inc. (Canada)
Fundersnot available
KeywordsMedicineAdjuvantStage (stratigraphy)Delphi methodAdjuvant therapyAdverse effectOncologyPerioperativeInternal medicineNeoadjuvant therapyCancerSurgeryBreast cancer

Abstract

fetched live from OpenAlex

Anti-programmed death (ligand) 1 (anti-PD-(L)1) therapies were first introduced in the metastatic setting and have since been approved and reimbursed for treating early-stage cancers in the adjuvant, perioperative, and neoadjuvant settings in many cancer types. Current evidence supporting anti-PD(L)-1 retreatment after relapse with prior neoadjuvant and/or adjuvant anti-PD(L)1 therapy is limited and inconclusive. There is no guidance for clinicians on how and when to retreat with anti-PD-(L)1 therapies when anti-PD-(L)1 therapy was administered in the neoadjuvant and/or adjuvant setting. This study aimed to reach consensus on factors to guide decision-making regarding retreatment with anti-PD-(L)1 therapies after prior therapy with an anti-PD-(L)1 agent. This modified Delphi study consisted of a clinician survey across 10 countries followed by three real-time virtual Delphi panels involving clinical experts who had completed the survey. Clinical experts were experienced in using anti-PD-(L)1 treatments in early-stage cancers and/or as retreatment of patients with recurrences following early-stage treatment with anti-PD-(L)1 therapies. Of 28 clinicians providing survey responses, 20 participated in one of three Delphi panels. There was consensus that retreatment can be defined as 'repeated treatment with the same therapeutic class following relapse after or during neoadjuvant and/or adjuvant treatment.' All three panels agreed that decisions around retreatment should consider 'prior immune-related adverse events/toxicity,' 'time-related factors' (eg, time since completion of full treatment course and since discontinuation) and 'previous patient response' (often referred to by clinicians as tumor response, which may have reflected their experience with metastatic disease). Other factors identified as important included country-specific practices, treatment availability, and reimbursement. Generally, the clinical experts considered that retreatment could be considered from ≥3 to 6 months after stopping initial anti-PD-(L)1 treatment, or from ≥6 months after relapse/recurrence. In conclusion, clinicians across different regions recognized a role for retreating patients with anti-PD-(L)1 therapies after initial anti-PD-(L)1 treatment for early-stage cancers. Consensus was reached on some factors to consider regarding whether and when to retreat, although differences in clinical practice between countries/geographical regions made it difficult to achieve consensus for some more nuanced elements of retreatment. Further evidence could help better inform retreatment decisions.

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)0.120
metaresearch head score (Gemma)0.158
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.635

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1200.158
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0020.003
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.392
Teacher spread0.350 · 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 designQualitative
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

Citations5
Published2025
Admission routes1
Has abstractyes

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