Evolving landscape of research on cancer-related cognitive impairment: A bibliometric analysis
Bibliographic record
Abstract
Objective: This study describes the state of the art in the field of cancer-related cognitive impairment (CRCI) to facilitate research opportunities in future CRCI research. Methods: Five databases were searched: PubMed, Web of Science, Cochrane Library, Cumulative Index to Nursing and Allied Health (CINAHL), and PsycINFO, from inception to August 20, 2022. Python, VOSviewer, and CiteSpace software were used for data preprocessing and analysis. Results: The published articles were predominantly from the United States, followed by China and Canada. Breast cancer and brain tumors were the dominant cancer types. The study population consisted mainly of adult cancer survivors. Prospective and multicenter studies were the most frequently used study designs. Keyword co-occurrence and mutation analysis indicated major themes: drug therapy was the most common treatment cluster, and adverse effects were another major cluster. The etiology of CRCI was a research hotspot and included the exploration of chemotherapy-associated and psychosocial factors by using measurement tools, such as neuropsychological tests and treatment outcomes. Conclusions: This study's findings highlight CRCI as a major research area, on the basis of the significantly increasing number of annual publications. Keyword co-occurrence analysis provided a quantitative visualization of the current research status for CRCI, but this method cannot provide in-depth qualitative insights explaining the potential emerging trends in this field.
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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 | 0.034 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.176 | 0.224 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".