Oncologists’ perception of depressive symptoms in patients with advanced cancer: accuracy and relational correlates
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Health care providers often inaccurately perceive depression in cancer patients. The principal aim of this study was to examine oncologist-patient agreement on specific depressive symptoms, and to identify potential predictors of accurate detection. METHODS: 201 adult advanced cancer patients (recruited across four French oncology units) and their oncologists (N = 28) reported depressive symptoms with eight core symptoms from the BDI-SF. Various indices of agreement, as well as logistic regression analyses were employed to analyse data. RESULTS: For individual symptoms, medians for sensitivity and specificity were 33% and 71%, respectively. Sensitivity was lowest for suicidal ideation, self-dislike, guilt, and sense of failure, while specificity was lowest for negative body image, pessimism, and sadness. Indices independent of base rate indicated poor general agreement (median DOR = 1.80; median ICC = .30). This was especially true for symptoms that are more difficult to recognise such as sense of failure, self-dislike and guilt. Depression was detected with a sensitivity of 52% and a specificity of 69%. Distress was detected with a sensitivity of 64% and a specificity of 65%. Logistic regressions identified compassionate care, quality of relationship, and oncologist self-efficacy as predictors of patient-physician agreement, mainly on the less recognisable symptoms. CONCLUSIONS: The results suggest that oncologists have difficulty accurately detecting depressive symptoms. Low levels of accuracy are problematic, considering that oncologists act as an important liaison to psychosocial services. This underlines the importance of using validated screening tests. Simple training focused on psychoeducation and relational skills would also allow for better detection of key depressive symptoms that are difficult to perceive.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it