Evaluation of Cognitive Function Associated With Chemotherapy: A Review of Published Studies and Recommendations for Future Research
Bibliographic record
Abstract
PURPOSE: There is evidence that some cancer survivors suffer cognitive impairment after chemotherapy. Determining if a patient has cognitive impairment is challenging, especially because impairment is usually subtle. PATIENTS AND METHODS: We assessed the design of studies evaluating cognitive function during or after chemotherapy in adult patients with solid tumors. We also reviewed methods used to evaluate cognitive function in subjects with other diseases and make recommendations for future studies. RESULTS: We identified 22 studies that met our criteria: 82% included women with breast cancer. Eight studies were longitudinal, 12 were cross-sectional, and two were follow-ups of cross-sectional studies. Sixteen studies used a battery of neuropsychological (NP) tests to assess subjects, and 13 included a control group. Ten studies (45%) had no explicit definition of cognitive impairment; most others used z scores or T scores and defined impairment based on standard deviations below the mean, but there was no consistency in for the cutoff point used or the number of tests required. CONCLUSION: There is no consistency in defining cognitive impairment, in the NP batteries used, or in statistical methods in studies of cognitive function of cancer patients. We suggest guidelines to define criteria for cognitive impairment. Use of summary scores and control groups is recommended. Practice effect should be adjusted for in longitudinal studies. A balance is needed between comprehensive batteries and briefer tests, which still need to be sensitive to mild impairment.
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 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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".