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Record W2022487683 · doi:10.1188/12.cjon.163-169

The Impact of Chemotherapy-Induced Cognitive Impairment on the Psychosocial Adjustment of Patients With Nonmetastatic Colorectal Cancer

2012· article· en· W2022487683 on OpenAlexaffabout
Jacqueline Galica, Dale Rajacich, Debbie Kane, Gregory R. Pond

Bibliographic record

VenueClinical journal of oncology nursing · 2012
Typearticle
Languageen
FieldMedicine
TopicCancer-related cognitive impairment studies
Canadian institutionsTrent University
Fundersnot available
KeywordsPsychosocialMedicineCambridge Neuropsychological Test Automated BatteryColorectal cancerCognitionContext (archaeology)CancerChemotherapyNeuropsychologyClinical psychologyOncologyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Colorectal cancer is the third most commonly diagnosed cancer in Canada. Chemotherapy often is used as treatment for colorectal cancer, and studies have documented cognitive changes in patients after chemotherapy treatment. What remains unclear is the impact of such changes on a person's roles and relationships, herein referred to as psychosocial adjustment. The purpose of this research was to explore group differences in psychosocial adjustment and chemotherapy-induced cognitive impairment in patients with colorectal cancer. Participants were assessed cross-sectionally, at various time points along their treatment trajectory, using the Psychosocial Adjustment to Illness Scale-Self-Report (PAIS-SR) and the Cambridge Neuropsychological Test Automated Battery (CANTAB). A statistically nonsignificant negative association was indicated between PAIS-SR and CANTAB results, indicating that they would have no meaning in a clinical context. No differences between groups were observed in terms of cognitive ability; however, patients who completed chemotherapy appeared to be at a higher risk for psychosocial maladjustment. This study suggests that cognitive changes do not influence patients' relationships and functional roles, as indicated from the PAIS-SR.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.051
GPT teacher head0.449
Teacher spread0.398 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations10
Published2012
Admission routes2
Has abstractyes

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