Cognitive impairment in adolescent and young adult cancer patients: Pre‐treatment findings of a longitudinal study
Bibliographic record
Abstract
BACKGROUND: There is little information about cancer-related cognitive impairment (CRCI) in adolescent and young adults (AYA, 15-39 years old) due to its rare incidence. Here, we present the pre-treatment (before chemotherapy or radiotherapy) evaluation of cognitive function and ability of AYA with cancer (AYAC) in a multicentered cohort study. METHODS: Newly diagnosed AYAC and age-matched healthy controls (HC) were recruited between 2018 and 2021. The primary outcome was the comparison of pre-treatment cognitive impairment defined as 2 standard deviations (SDs) below the HC on ≥1 cognitive test, or >1.5 SDs below on ≥2 tests using CANTAB® between AYAC and HC. Secondary outcomes included self-perceived cognitive ability assessed by FACT-Cog v3 and biomarkers (inflammatory cytokines and brain-derived neurotrophic factor [BDNF]). RESULTS: We recruited 74 AYAC (median age = 34) and 118 HC (median age = 32). On objective cognitive testing, we observed three times more AYAC patients performed poorly on at least 2 cognitive tests compared to HC (40.5% vs. 13.6%, p < 0.001). AYAC self-perceived less degree of cognitive impairment than HC (p < 0.001). However, AYAC perceived a greater impact of cognitive changes on their quality of life compared to HC (p = 0.039). Elevated baseline inflammatory markers (IL-2, IL-4, IL-6, IL-8, IL-10 and IFN-γ) were observed among AYAC compared to HC, and baseline BDNF was lower in AYAC compared to HC. Interaction effects between cancer diagnosis and biomarkers were observed in predicting cognitive function. CONCLUSION: With the pre-existence of CRCI and risk factors of neuroinflammation even prior to systemic therapy, AYAC should receive early rehabilitation to prevent further deterioration of cognitive function after initiation of systemic therapies. (ClinicalTrials.gov Identifier: NCT03476070).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".