Selective Serotonin Reuptake Inhibitor and Serotonin-Noradrenaline Reuptake Inhibitor Withdrawal Changes DSM Presentation of Mental Disorders: Results from the Diagnostic Clinical Interview for Drug Withdrawal
Bibliographic record
Abstract
INTRODUCTION: Selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs) may cause withdrawal at dose decrease, discontinuation, or switch. Current diagnostic methods (e.g., DSM) do not take such phenomenon into account. Using a new nosographic classification of withdrawal syndromes due to SSRI/SNRI decrease or discontinuation [by Psychother Psychosom. 2015;84(2):63-71], we explored whether DSM is adequate to identify DSM disorders when withdrawal occurs. METHODS: Seventy-five self-referred patients with a diagnosis of withdrawal syndrome due to discontinuation of SSRI/SNRI, diagnosed via the Diagnostic Clinical Interview for Drug Withdrawal 1 - New Symptoms of Selective Serotonin Reuptake Inhibitors or Serotonin-Norepinephrine Reuptake Inhibitors (DID-W1), and at least one DSM-5 diagnosis were analyzed. RESULTS: In 58 cases (77.3%), the DSM-5 diagnosis of current mental disorder was not confirmed when the DID-W1 diagnosis of current withdrawal syndrome was established. In 13 cases (17.3%), the DSM-5 diagnosis of past mental disorder was not confirmed when criteria for DID-W1 diagnosis of lifetime withdrawal syndrome were met. In 3 patients (4%), the DSM-5 diagnoses of current and past mental disorders were not confirmed when the DID-W1 diagnoses of current and lifetime withdrawal syndromes were taken into account. The DSM-5 diagnoses most frequently mis-formulated were current panic disorder (50.7%, n = 38) and past major depressive episode (18.7%, n = 14). CONCLUSION: DSM needs to be complemented by clinimetric tools, such as the DID-W1, to detect withdrawal syndromes induced by SSRI/SNRI discontinuation, decrease, or switch, following long-term use.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".