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Record W2801445342 · doi:10.1002/hed.25198

Pretreatment neurocognitive function and self‐reported symptoms in patients with newly diagnosed head and neck cancer compared with noncancer cohort

2018· article· en· W2801445342 on OpenAlexafffund
Lori J. Bernstein, Gregory R. Pond, Hui Gan, Kattleya Tirona, Kelvin Chan, Andrew Hope, John Kim, Eric X. Chen, Lillian L. Siu, Albiruni R. Abdul Razak

Bibliographic record

VenueHead & Neck · 2018
Typearticle
Languageen
FieldMedicine
TopicCancer-related cognitive impairment studies
Canadian institutionsPrincess Margaret Cancer CentreMcMaster UniversityUniversity of Toronto
FundersCanadian Institutes of Health ResearchPrincess Margaret Cancer Foundation
KeywordsMedicineNeurocognitiveHead and neck cancerCohortAnxietyInternal medicineCancerMoodNeuropsychologyCognitionNeuropsychological testEffects of sleep deprivation on cognitive performanceDepression (economics)Cohort studyOncologyClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.267
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2018
Admission routes2
Has abstractyes

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