Depression assessment and classification in palliative cancer patients: a systematic literature review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The objective of this study was to review the literature on depression in palliative cancer care in order to identify which assessment methods and classification systems have been used in studies of depression. Extensive electronic database searches in PubMed, CancerLit, CINAHL, PsychINFO, EMBASE and AgeLine as well as hand search were carried out. In the 202 included papers, 106 different assessment methods were used. Sixty-five of these were only used once. All together, the Hospital Anxiety and Depression Scale (HADS) was the most commonly used assessment method. However, there were regional differences and while the HADS dominated in Europe it was quite seldom used in Canada or in the USA. Few prevalence and intervention studies used assessment methods with an explicit reference to a diagnostic system. There were in total few case definitions of depression. Among these, the classifications were in general based on cut-off scores (77%) and not according to diagnostic systems. The full range of the DSM-IV diagnostic criteria was seldom assessed, i.e. less than one-third of the assessments in the review took into account the duration of symptoms and 18% assessed consequences and impact upon patient functioning. A diversity of assessment methods had been used. Few studies classified depression by referring to a diagnostic system or by using cut-off scores. Evidently, there is a need for a consensus on how to assess and conceptualize depression and related conditions in palliative care.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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