Cancer Related Cognitive Impairment in Breast Cancer Survivors: A Case - Control Study of Karnataka, India
Bibliographic record
Abstract
A BSTRACT Background: Breast cancer patients report difficulties with concentration, multi-tasking, and memory. Cognitive dysfunction can impact the quality of life by affecting activities of daily living, treatment compliance, interpersonal relationships, and profession. Aims: The objective of this study was to evaluate and compare the cognitive functions and psychological complications in breast cancer patients with a control population. Settings and Design: This study was conducted in the outpatient department of Bharath Hospital and Institute of Oncology, Mysore, where the cases were recruited. The study participants were 110 female breast cancer patients and 100 noncancer healthy females as controls. Materials and Methods: Demographic details of the participants were collected through a questionnaire. Clinical data were obtained from clinical records. Tools used were Montreal Cognitive Assessment (MoCA), Hamilton Depression Rating Scale (HAM-D), and Hamilton Anxiety Rating Scale (HAM-A) to evaluate cognitive functions, depression, and anxiety, respectively. Statistical Analysis: The various findings were analyzed using mean, frequency, Pearson’s correlation, and two-sample t -test. Results: Mild cognitive impairment was observed in 88% of the cancer patients, and more than 95% were under severe anxiety and depression. There is a highly significant difference in all three tests (MoCA Test, HAM-D test, HAM-A test) with a P < 0.001. Conclusion: Breast cancer patients show statistically significant cognitive deficits as compared to noncancer individuals.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".