Inconsistencies between measures of cognitive dysfunction in childhood acute lymphoblastic leukemia survivors: Description and understanding
Bibliographic record
Abstract
OBJECTIVES: The frequency of cognitive difficulties in childhood cancer survivors varies according to the measurement strategy. The goal of this research is to (a) describe agreements and differences between measures of working memory and attention (b) identify contributors of these differences, such as emotional distress, affects, and fatigue. METHODS: We used data available for 138 adults successfully treated for childhood acute lymphoblastic leukemia (ALL) (PETALE cohort). Working memory and attention were assessed using subtests from the WAIS-IV and self-reported questionnaires (BRIEF-SR and CAARS-S:L). Potential contributors included emotional distress, anxiety, depression (BSI-18), affects (PANAS), and fatigue (PedsQL-MFS). We explored measurement agreements and differences using diagnostic indices and multivariate regression models. RESULTS: The frequencies of working memory and attention deficits were higher when using cognitive tests (15%-21%) than with self-reports (10%-11%). Self-reported questionnaires showed high specificity (median 0.87) and low sensitivity (median 0.10), suggesting they did not reliably identify positive cases on cognitive tests. We identified negative affectivity as a possible contributor to inconsistencies between self-report and test results. CONCLUSIONS: When measuring working memory and attention in childhood ALL survivors, cognitive test results and self-reports should not be considered equivalent. At best, self-report may be used for screening (high specificity), but not to assess prevalence in large samples. Self-reported difficulties are also probably influenced by the negative mood in this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".