Bibliographic record
Abstract
During and following chemotherapy, some patients experience difficulties with memory, attention, and other aspects of cognitive function. This constellation of deficits commonly is referred to as chemo brain. Although the phenomenon is not understood completely, it is assuming greater significance as cancer survival improves. Return to prediagnosis levels of domestic, employment, and academic activity is expected in most survivors. Advances in basic, imaging, and clinical sciences are beginning to unravel pathophysiologic mechanisms and develop neuroprotective strategies. Pharmacologic options are borrowed from diverse diseases, including attention-deficit/hyperactivity disorder and neurodegenerative diseases. Conventional therapies soon may find new applications; for example, recent preclinical data suggest that erythropoietin may have some neuroprotective abilities, which may positively affect patients experiencing chemo brain. A collaborative model is bringing together international specialists interested in unraveling the mysteries of the phenomenon and developing management strategies to attenuate its effects. This article will review the clinical features of chemo brain as well as a working hypothesis regarding pathophysiology. The potential and emerging interventions that can be used by oncology nurses to assist patients and their families to cope with this enigmatic dysfunction will be discussed.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".