Clinical Assessment of Cognitive Function in Patients with Head and Neck Cancer: Prevalence and Correlates
Bibliographic record
Abstract
Objective Identify the prevalence and clinical correlates of cognitive impairment in patients presenting for treatment of head and neck cancer (HNC) using brief screening within a multidisciplinary care team. Study Design A case series with planned data collection of cognitive function, quality of life (QoL), and psychosocial variables. Setting Urban Midwest academic medical center. Subjects and Methods In total, 209 consecutive patients with a diagnosis of HNC between August 2015 and September 2016 who had a pretreatment assessment with a clinical health psychologist. At pretreatment assessment, the Montreal Cognitive Assessment (MoCA), a brief screening tool for cognitive function, was administered along with a semistructured interview to gather information on psychiatric symptoms, social support, and substance use. Patient information, including demographics, clinical variables, and psychosocial variables, was extracted via chart review. A subset of patients with HNC completed the Functional Assessment of Cancer Therapy-Head and Neck Cancer at pretreatment assessment and was included in the QoL analyses. Results Cognitive impairment was associated with current alcohol use, past tobacco use and number of pack years, time in radiotherapy, and adherence to treatment recommendations. Social, emotional, and functional QoL scales were associated with cognitive impairment, including executive function, language, and memory. Conclusion Cognitive impairment is common in patients with HNC, and there are important associations between cognitive impairment and psychosocial, QoL, and treatment adherence variables. The results argue for the incorporation of cognitive screening as part of pretreatment assessment for patients, as well as further research into more direct, causal relationships via longitudinal, prospective studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".