The Motivation and Energy Inventory (MEI): Analysis of the clinically relevant response threshold in patients with major depressive disorder and emotional blunting using data from the COMPLETE study
Bibliographic record
Abstract
BACKGROUND: The Motivation and Energy Inventory (MEI) is a patient-reported scale for assessment of the impact of mental/cognitive energy, social motivation, and physical energy on daily functioning in patients with major depressive disorder (MDD). This analysis was undertaken to establish the clinically relevant response threshold for the MEI in patients with MDD receiving antidepressant treatment. METHODS: Patients with MDD experiencing inadequate response and emotional blunting on selective serotonin reuptake inhibitor or serotonin-noradrenaline reuptake inhibitor monotherapy (adequate dose for ≥6 weeks) were switched to vortioxetine 10-20 mg/day for 8 weeks. Anchor- and distribution-based methods were used to determine the minimal clinically important difference (MCID) in MEI total score. RESULTS: After 8 weeks of vortioxetine treatment, mean (standard deviation) change in MEI total score from baseline was 33.0 (27.3) points. At week 8, mean change in MEI total score from baseline was 37.5 (27.8) points in patients no longer reporting emotional blunting and 28.3 (26.2) points in those still experiencing emotional blunting. In patients considered minimally improved (i.e. Clinical Global Impression-Improvement [CGI-I] score of 3 after 8 weeks of vortioxetine), mean change in MEI total score from baseline was 14.7 (19.1) points. In patients defined as responders (CGI-I score of 2 at 8 weeks), mean change in MEI total score was 33.0 (24.7) points. LIMITATIONS: Short study duration. CONCLUSIONS: These results provide further validation of the clinical utility of the MEI for assessing treatment response in patients with MDD. The suggested MCID for MEI total score is 15 points. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT03835715.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".