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Cognitive Symptoms Across Diverse Cancers

2024· article· en· W4401948790 on OpenAlexafffundabout
Samantha Mayo, Kim Edelstein, Eshetu G. Atenafu, Rand Ajaj, Madeline Li, Lori J. Bernstein

Bibliographic record

VenueJAMA Network Open · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer-related cognitive impairment studies
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersAssociation canadienne des infirmières en oncologiePrincess Margaret Cancer Foundation
KeywordsMedicinePsychosocialCancerBreast cancerCognitionDepression (economics)Outpatient clinicPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Importance: Psychosocial health services for adults with cancer should include support for cognitive symptoms and symptom clusters. Objective: To characterize the frequency and severity of cognitive symptoms and to identify demographic and clinical risk factors associated with moderate to severe cognitive symptoms among outpatient adults with cancer seeking psychosocial support. Design, Setting, and Participants: This cross-sectional study analyzed data from routine patient-reported symptom screening during clinic appointments at the Princess Margaret Cancer Centre in Toronto, Canada, between January 1, 2013, and December 31, 2019. Participants were outpatient adults (aged ≥18 years) with diverse cancer diagnoses who endorsed interest in receiving psychosocial support from a health care team member. Data analysis was performed from April 2020 to June 2024. Main Outcomes and Measures: The presence and severity of cognitive symptoms as self-rated by participants were characterized across 12 cancer types: brain or central nervous system (CNS), breast, gastrointestinal, head and neck, gynecological, thyroid, lung and bronchus, sarcoma, genitourinary, melanoma, hematological, and all other cancers. Multivariable logistic regression was used to explore the associations between demographic, clinical, and symptom factors and moderate to severe cognitive symptoms. Results: Across the sample of 5078 respondents (2820 females [55.5%]; mean [SD] age at time of survey, 56.0 [14.1] years) requesting psychosocial support, 3480 (68.5%) reported cognitive symptoms of any severity, ranging from 59.5% in sarcoma to 86.5% in brain or CNS cancer. Moderate to severe cognitive symptoms were reported by 1544 patients (30.4%), with the proportions being 51.3% for patients with brain or CNS, 37.0% for breast, 36.2% for thyroid, 30.9% for melanoma, 29.6% for head and neck, 28.3% for gastrointestinal, 28.2% for hematological, 28.1% for gynecological, 24.9% for lung and bronchus, 24.9% for sarcoma, 21.0% for genitourinary, and 26.8% for all other cancers. Across the entire sample, moderate to severe cognitive symptoms were associated with recurrence or progression involving the CNS (odds ratio [OR], 2.62; 95% CI, 1.80-3.81), depression (OR, 1.92; 95% CI, 1.59-2.31), tiredness (OR, 1.82; 95% CI, 1.52-2.19), drowsiness (OR, 1.64; 95% CI, 1.39-1.93), anxiety (OR, 1.57; 95% CI, 1.30-1.89), shortness of breath (OR, 1.38; 95% CI, 1.16-1.61), female sex (OR, 1.33; 95% CI, 1.14-1.56), first-line chemotherapy received (OR, 1.22; 95% CI, 1.05-1.41), and metastatic disease at diagnosis (OR, 0.74; 95% CI, 0.61-0.89). Within individual cancer types, tiredness and depression were consistently associated with moderate to severe cognitive symptoms. Conclusions and Relevance: This cross-sectional study found that cognitive symptoms were frequently reported by patients across a wide range of cancer types; higher severity of cognitive symptoms was consistently associated with higher symptom burden. The findings could be used to inform decision-making regarding access to cognitive screening, assessment, and supportive care in outpatient oncology clinics.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.032
GPT teacher head0.365
Teacher spread0.334 · 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 designNot applicable
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

Citations19
Published2024
Admission routes3
Has abstractyes

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