Depression in the medically ill
Bibliographic record
Abstract
BACKGROUND: Depressive disorders are significantly more common in the medically ill compared to the general population. Depression is associated with worsening of physical symptoms, greater healthcare utilization and poorer treatment adherence. The present paper provides a critical review on the assessment and management of depression in the medically ill. METHODS: Relevant articles pertaining to depression in the medically ill were identified, reviewed and synthesized qualitatively. A systematic review was not performed due to the large breadth of this topic, making a meaningful summary of all published and unpublished studies not feasible. Notable studies were reviewed and synthesized by a diverse set of experts to provide a balanced summary. RESULTS: Depression is frequently under-recognized in medical settings. Differential diagnoses include delirium, personality disorders and depressive disorders secondary to substances, medications or another medical condition. Depressive symptoms in the context of an adjustment disorder should be initially managed by supportive psychological approaches. Once a mild to moderate major depressive episode is identified, a stepped care approach should be implemented, starting with general psychoeducation, psychosocial interventions and ongoing monitoring. For moderate to severe symptoms, or mild symptoms that are not responding to low-intensity interventions, the use of antidepressants or higher intensity psychotherapeutic interventions should be considered. Psychotherapeutic interventions have demonstrated benefits with small to moderate effect sizes. Antidepressant medications have also demonstrated benefits with moderate effect sizes; however, special caution is needed in evaluating side effects, drug-drug interactions as well as dose adjustments due to impairment in hepatic metabolism and/or renal clearance. Novel interventions for the treatment of depression and other illness-related psychological symptoms (e.g. death anxiety, loss of dignity) are under investigation. LIMITATIONS: Non-systematic review of the literature. CONCLUSION: Replicated evidence has demonstrated a bidirectional interaction between depression and medical illness. Screening and stepped care using pharmacological and non-pharmacological interventions is merited.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".