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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".