Pretreatment neurocognitive function (NCF) status in head and neck cancer (HNC) patients (pts) with comparison to control cohort.
Bibliographic record
Abstract
5587 Background: There is increasing evidence that NCF abnormalities may occur in cancer pts. Data on pre-treatment NCF in HNC pts are lacking. This study reports NCF in pts with newly diagnosed, curable HNC compared to controls. Methods: HNC pts underwent a 2-hour battery of NCF tests prior to radio +/-chemo(bio)therapy. Domains tested were intelligence (IQ), memory, language, attention, processing speed, executive function and manual dexterity. Test performances were transformed into Z-scores using normative data (score < -1 signified deficit). Pts also had self-reported assessments for NCF, quality of life (QOL), fatigue and affect. Data obtained were compared to non-cancer controls who underwent the same tests. Results: Eighty HNC and 30 control subjects were assessed. Objective NCF testing demonstrated that HNC and control cohorts were similar across all domains, except for IQ, with pts having higher scores (mean 0.55 vs 0.12, p=0.03). However, individual analysis showed that 39% of HNC and 43% of control subjects had abnormal Z-scores in ≥ 2 domains. Multivariable analysis of factors associated with ≥ 2 abnormal NCF domains included: low education level, significant smoking history (≥ 10 pack year), previous mild brain injury, gender, and group (pt vs control). Amongst pts, HPV -ve status and non-oropharyngeal tumors were also associated with decreased NCF. Pts reported statistically worse subjective baseline symptoms compared to controls: NCF (mean FACT-COG 33.7 vs 18.2, p=0.002), QOL (FACT H&N 33.8 vs 14.9), fatigue (FACT-F 35.1 vs 15.1), anxiety (HADS 7.0 vs 3.1) and depression (HADS 3.9 vs 1.2), p<0.01 for all five parameters. Conclusions: Objectively assessed NCF was similar between HNC pts and controls, but a proportion of participants in both cohorts have multi-domain abnormal Z-scores. Several patient demographics and disease characteristics were associated with abnormal NCFs. Subjectively, pts reported worse NCF, QOL, fatigue and affect. These data suggest that participant and disease characteristics may play a larger role in determining NCF than previously shown. Whether such characteristics impact subsequent NCF is under investigation in a longitudinal study.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".