Pretreatment neurocognitive function and self‐reported symptoms in patients with newly diagnosed head and neck cancer compared with noncancer cohort
Bibliographic record
Abstract
BACKGROUND: Newly diagnosed patients with head and neck cancer may be at risk for impaired neurocognitive function (NCF) due to disease, treatment, and lifestyle factors. METHODS: Eighty pretreatment patients with head and neck cancer and 40 control patients without cancer completed assessment of NCF and self-reported cognition, fatigue, and mood. Blood samples to evaluate organ reserves, hormones, and cytokines were collected. RESULTS: Patients experienced worse symptoms of cognitive dysfunction, fatigue, and anxiety than controls. In contrast, NCF was equivalent for patients and controls. Using published norms as comparison, groups had similar high rates of impairment in performance (9/80 patients and 3/40 controls scored in the abnormal range). CONCLUSION: Pretreatment patients with head and neck cancer reported cognitive disturbance. The frequency of impaired performance, albeit high, was consistent with the literature demonstrating false-positive "abnormal" neuropsychological test performance is not uncommon. Inclusion of a noncancer patient control cohort is essential because using solely normative data as a comparison may foster erroneous interpretation.
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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 | 0.002 |
| 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) | 0.001 | 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".