Major depressive disorder and associated factors in elderly patients with non-Hodgkin’s lymphoma
Bibliographic record
Abstract
Background: Data regarding elderly patients with cancer, more particularly with non-Hodgkin’s lymphomas (NHL), are scarce, as is our knowledge concerning to comorbid depression in this population. The purpose of this work was to explore the frequency of major depressive disorder (MDD) and related factors in a group of elderly patients with these forms of cancer.Method: 42 elderly NHL patients aged 70 years and older were interviewed using the Mini International Neuropsychiatric Interview screening tool. Psychological variables such as coping strategies, cognitive status and quality of relationships, as well as clinical and socio-demographic data were collected.Results: Fourteen patients (33.3%) met criteria for current MDD of which five had melancholy features (35.7%). Elderly patients with comorbid NHL-MDD had a significantly poorer self-perceived global health and performance status than those without MDD, as well as more fatigue and history of depression. No other clinical, psychological or socio-demographic variable appeared associated with MDD in this sample.Conclusion: Further studies are needed in order to confirm these early results concerning a potential high frequency of MDD among elderly NHL patients. Depressive mood should be early recognized in order to provide appropriate treatments and avoid a detrimental effect of depression on cancer prognosis.
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 | 0.000 | 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".