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A descriptive patient-reported outcomes (PROs) analysis of KEYNOTE-412 to understand head and neck symptom burden.

2024· article· en· W4399565437 on OpenAlexaff
Lillian L. Siu, Kelly McQuarrie, Behzad Bidadi, Chih‐Chin Liu, Christopher M. Black, Anran Wang, Yungan Tao, Lisa Licitra, Barbara Burtness, Makoto Tahara, Danny Rischin, Kevin J. Harrington, Jean-Pascal Machiels

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldHealth Professions
TopicMedical Practices and Rehabilitation
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineHead and neckDescriptive statisticsHead and neck cancerPatient-reported outcomePhysical therapyIntensive care medicineQuality of life (healthcare)SurgeryInternal medicineCancerNursing

Abstract

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11116 Background: The randomized, double-blind, phase 3 KEYNOTE-412 study (NCT03040999) investigated pembrolizumab (pembro) + chemoradiation therapy (CRT) versus placebo + CRT for locally advanced head and neck squamous cell carcinoma (LA HNSCC). Descriptive PRO analysis may provide important insight to better understand patient (pt) experiences. This analysis evaluated pt experience by examining item-level PRO scores. Methods: Adults with LA HNSCC were randomly assigned 1:1 to pembro 200 mg IV Q3W or placebo + CRT. PROs included the EORTC QLQ-C30 and EORTC QLQ-H&N35. Responses to each item of the QLQ-C30 and H&N35 were analyzed descriptively based on treatment group combined data. Each item was evaluated overall, and then for the H&N35 by tumor location and by clinical event (death, distant progressive disease [PD], local regional PD, local PD, and distant metastasis or histologically persistent/residual disease). Symptom burden was defined as responses of “a little,” “quite a bit,” “very much,” or “yes.” Items associated with high symptom burden (≥30% of responses “quite a bit,” “very much,” or “yes”) at baseline were analyzed over time. Results: Of 756 pts with PRO data, highly endorsed items (≥50% experiencing the symptom) on the QLQ-C30 at baseline were pain, trouble sleeping, needing rest, tiredness, and worry; highly endorsed (≥50%) items from the H&N35 were pain in mouth, painful throat, problems swallowing solid food, coughing, hoarseness, and use of pain killers. Pts with an event, compared to pts who were event-free at the time of data cut-off, experienced greater symptom burden at baseline; for example, pain in mouth (56% vs 50%), pain in jaw (47% vs 37%), use of pain killers (71% vs 58%), weight loss (53% vs 30%), trouble eating (63% vs 46%), and problems swallowing solid food (69% vs 56%). Symptoms at baseline also differed by primary tumor location, demonstrating the heterogeneity of symptom burden across this population. Based on the threshold of ≥30% reporting worse responses at baseline, 20 items on the H&N35 were analyzed over time. Of pts who experienced an event (n = 345, 46%), 6 items met this threshold: pain in mouth (32%), problems swallowing solid food (41%), trouble eating (32%), use of pain killers (71%), use of nutritional supplements (34%), and weight loss (53%). For these items, a similar or greater percentage of pts with symptoms were observed over time in those who had events compared to those with no events. Conclusions: Observations of pts enrolled in KEYNOTE-412 study who had events, appearing to have greater symptom burden at baseline, suggests there may be a relationship between baseline item scores and clinical outcomes. Heterogeneity of populations may lead to variability of baseline PRO scores and should be considered in PRO analysis. Further research is needed to explore the relationship between baseline PROs and event outcomes as well as additional subgroup analysis. Clinical trial information: NCT03040999 .

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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)0.009
metaresearch head score (Gemma)0.009
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: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.282
GPT teacher head0.596
Teacher spread0.313 · 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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