The impact of genetic predisposition to depression on quality of life in patients with head and neck cancer immediately post-treatment: A longitudinal study.
Bibliographic record
Abstract
6063 Background: The primary aim of this study was to investigate the contribution of genetic predisposition to depression, through polygenic risk scores (PRS), on quality of life levels in patients with head and neck cancer (HNC) immediately post-treatment period (i.e., 3 months post-diagnosis). Methods: Prospective longitudinal study of 223 consecutive adult patients with HNC (72% participation) newly diagnosed with a first occurrence of primary HNC, including saliva samples analyzed using the Illumina PsychChip, psychometric measures, Structured Clinical DSM Interviews, and medical chart reviews. Results: Level of quality of life at 3 months on the FACT-G+H&N was predicted by (r2 = 0.51, r2 adj. = 0.33, p = 0.001) the polygenic risk score for depression (standardized b = -0.28, p = 0.01) and a previous history of suicidal ideation (standardized b = -0.25, p = 0.04). Other variables were non-significant in the analyses: sociodemographic (i.e., age, sex, education, living alone), psychosocial (i.e., SCID current and past diagnoses (trend), past history of abuse), and medical variables (i.e., cancer stage and site, HPV status, functional status/ECOG, treatment). Conclusions: Our results outline the importance of attending to genetic predisposition and past history of suicidal ideation as markers for quality of life compromise immediately post-treatment in patients with head and neck cancers. Strategies are needed to address psychosocial vulnerability early-on as part of pre-habilitation in the treatment of patients with head and neck cancer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".