Disease stage predicts post-diagnosis anxiety and depression only in some types of cancer
Bibliographic record
Abstract
BACKGROUND: We hypothesised that patients with advanced disease or a cancer type that has a poor prognosis may be more likely to report anxiety and depressive symptoms after diagnosis; younger age and female gender may moderate these effects. METHODS: Patients (n=3850) were consecutively assessed with PSSCAN, a standardised, validated tool, at two large cancer centres between 2004 and 2009. RESULTS: Female patients reported more anxiety and depressive symptoms (P=0.003 to P<0.001) compared with men and a healthy comparison group. Older age was associated with fewer anxiety (P=0.033 to P<0.001) and fewer depressive symptoms (P<0.001), but this was not true for lung cancer. Presence of metastases was associated with more anxiety symptoms in patients with gastrointestinal (P=0.044; R(2)Δ=0.001), lung (P=0.011; R(2)Δ=0.016), and prostate (P=0.032; R(2)Δ=0.008) cancer, but this was not true for breast cancer. Furthermore, early disease stage was associated with fewer depressive symptoms among older prostate cancer patients (P=0.021; R(2)Δ=0.008). Men with early lung cancer reported fewer anxiety (P=0.020; R(2)Δ=0.013) and depressive (P=0.017; R(2)Δ=0.016) symptoms than men with advanced disease or women. CONCLUSION: As hypothesised, disease stage was directly associated with emotional distress, except for patients with breast cancer. Furthermore, age and gender moderated some of these effects.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".