A Preliminary Validation of an Optimal Cutpoint in Total Number of Patient-Reported Symptoms in Head and Neck Cancer for Effective Alignment of Clinical Resources With Patients’ Symptom Burden
Bibliographic record
Abstract
Background: Patients with head and neck cancer (HNC) often experience high symptom burden leading to lower quality of life (QoL). Objective: This study aims to conceptually model optimal cutpoint by examining where the total number of patient-reported symptoms exceeds patients’ coping capacity, leading to a decline in QoL in patients with HNC. Methods: Secondary data analysis of 105 individuals with HNC enrolled in a clinical usefulness study of the NYU Electronic Patient Visit Assessment (ePVA), a digital patient-reported symptom measure. Patients completed ePVA and European Organization for Research and Treatment of Cancer (EORTC) QLQ-C30 v3.0. The total number of patient-reported symptoms was the sum of symptoms as identified by the ePVA questionnaire. Analysis of variance was used to define the optimal cutpoint. Results: Study participants had a mean age of 61.5, were primarily male (67.6%), and had stage IV HNC (53.3%). The cutpoint of 10 symptoms was associated with a significant decline of QoL (F = 44.8, P < .0001), dividing the population into categories of low symptom burden (<10 symptoms) and high symptom burden (≥10 symptoms). Analyses of EORTC function subscales supported the validity of 10 symptoms as the optimal cutpoint (physical: F = 28.3, P < .0001; role: F = 21.6, P < .0001; emotional: F = 9.5, P = .003; social: F = 33.1, P < .0001). Conclusions: In HNC, defining optimal cutpoints in the total number of patient-reported symptoms is feasible. Implications for Practice: Cutpoints in the total number of patient-reported symptoms may identify patients experiencing a high symptom burden from HNC. What is Foundational: Using optimal cutpoints of the total number of patient-reported symptoms may help effectively align clinical resources with patients’ symptom burden.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".