Neurocognitive outcomes of head and neck chemoradiotherapy: A pilot study
Bibliographic record
Abstract
6068 Background: Evidence suggests cancer-related treatments affect cognition. To our knowledge, no studies have systematically investigated cognitive impairment in head and neck cancer (HNC) patients (pts). We assessed ten relapse-free HNC pts after curative-intent radiotherapy (RT), half of whom received cisplatin (Cp). Methods: Pts completed a 2-hr battery of tests/questionnaires assessing objective cognitive function (CF), subjective CF, quality of life and affect. Objective measures of CF were transformed to Z-scores (mean=0, standard deviation=1) using age normative data. A negative value for the Difference Score (DS=Z-score minus IQ score) in each tested domain indicates cognitive deterioration as IQ is a pre-morbid estimate of pts’ CF. A Global Deficit Score (GDS) was obtained by averaging the DS of all tested CF domains. Results: Pt demographics were: M:F=8:2; mean age=58 yrs(range 47–66); mean smoking pack yrs=15(0–45); mean drinks/week=7(0–25); mean IQ Z-score=+1.2(-1.0 to +2.0), mean school yrs=15 (6–18) and mean time post treatment=20 mo(9–41). All pts completed the battery within 2 hr. Nine participants appeared to have impaired CF based on negative DS and GDS scores ( Table 1 ). Exploratory univariate analyses showed trends that higher RT dose and Cp use were associated with increased impairment but cytokines, anemia, hormonal status and affective state were not. Conclusions: This feasibility study suggests cancer-related treatment affects cognition in HNC survivors. A longitudinal study is underway. [Table: see text] No significant financial relationships to disclose.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".