The association between neuropsychological impairment, self-perceived cognitive deficit, symptoms, and health related quality of life in newly diagnosed ovarian cancer patients
Bibliographic record
Abstract
Objective: To assess cognitive function in patients newly diagnosed with ovarian cancer (OC) before treatment and explore the relationship between neuropsychological impairment, self-perceived cognitive deficit, symptoms, and health-related quality of life in them. Methods: From May 2021 to February 2022, 105 women newly diagnosed with OC were enrolled in the Cancer Center of Fudan University, Shanghai, China. Objective and subjective cognitive functions were assessed using the Montreal Cognitive Assessment (MoCA) scale and Perceptual Deficits Questionnaire (PDQ). Symptoms and quality of life were evaluated using the Memorial Symptom Assessment Scale (MSAS) and Functional Assessment of Cancer Therapy-Ovarian Cancer (FACT-O), respectively. Results: = 0.067) and significantly correlated with the distress index, physiological and total scores of the MSAS, and emotional well- being of the FACT-O. The PDQ positively correlated with all MSAS dimensions but not with the FACT-O. Conclusion: The incidence of neuropsychological impairment in patients newly diagnosed with OC was high, with no association with self-perceived cognitive deficits. It is recommended that healthcare providers include cognitive impairment in symptom management in this population, who may benefit from early assessment, prevention, and intervention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".