Reply to Depression as a predictor of disease progression and mortality in cancer patients
Bibliographic record
Abstract
In their correspondence, Schneider and Moyer commented on our meta-analysis of prospective studies regarding depression and cancer1 by calling attention to a previous meta-analysis by Cuijpers and Smit that we had cited.2 Cuijpers and Smit had demonstrated a greater association between depression and mortality in the general population than that presented in our meta-analysis, which was restricted to cancer patients. Schneider and Moyer concluded that the effect of depression on mortality may be potentially smaller in cancer patients compared with the general population, and suggested that cancer-specific mortality is unlikely to be affected by depression. We argue here that a discrepancy between meta-analytic findings derived from different populations does not rule out a potential cancer-specific association. The causes of death are not described in the meta-analysis by Cuijpers and Smit, and cardiac disease is likely to be a major cause of death in the community samples that had been studied. Given that the observed risk of depression and mortality in cardiac patients is roughly 2:13 and therefore much greater than the ratio we observed in cancer patients, and that cardiovascular disease accounts for nearly 50% of all deaths, the results in the meta-analysis by Cuijpers and Smit were likely driven by cardiac deaths in their community samples. A much-needed, large-scale prospective study that compares the association between depression and mortality outcomes across various causes of death would allow for a more definitive conclusion regarding this point. Schneider and Moyers also raised the question of implications for depression treatment when the observed risk is only moderately elevated and not very different from that of the population at large. We do know that depression negatively affects adherence to medical treatment protocols,4 and that the psychological treatment of depression can be efficacious in relieving distress.5 To us, these facts are ample justification for the proactive identification and treatment of depression in cancer patients, even if their mortality risk was neither cancer specific nor greater than that of the general population.
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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 | 0.018 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.031 | 0.047 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".