Can antidepressant use be associated with emotional blunting in a subset of patients with depression? A scoping review of available literature
Bibliographic record
Abstract
INTRODUCTION: Despite frequent recognition of emotional blunting in the published literature, either as a primary symptom of depression or as an adverse effect of antidepressants, there is no systematic synthesis on this topic to our knowledge. We undertook this scoping review to assess the prevalence, clinical features, implicated causes and management of emotional blunting, outlining the phenomenological and clinical gaps in research. METHOD: A systematic search was done until March 15, 2022, to include all original studies (i.e., interventional trials, cohort & cross-sectional studies, case reports, and case series). All reviewed data were delineated to answer pertinent clinical, phenomenological, and management questions related to the phenomenon of emotional blunting. RESULTS: A total of 25 original studies were included in our scoping review. Emotional blunting was described as a persistent diminution in both positive and negative feelings in depressed patients, who could subjectively differentiate it from their acute symptoms. However, the literature lacked the distinction between emotional blunting as a primary symptom of depression or an adverse effect of antidepressants. Common clinical strategies to manage antidepressant-induced emotional blunting included dose reduction or switching to a different antidepressant. CONCLUSION: Emotional blunting was a significant patient-reported concern with antidepressants. Future research should clarify phenomenological and neurobiological constructs underlying emotional blunting to improve diagnostic and management skills.
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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 | 0.009 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".