Lam Employment Absence and Productivity Scale (LEAPS): Analysis of the minimal clinically important difference (MCID) in patients with major depressive disorder using data from the CAN-BIND-1 study
Bibliographic record
Abstract
OBJECTIVES: Major depressive disorder (MDD) is associated with significant impairment in occupational functioning. The Lam Employment Absence and Productivity Scale (LEAPS) is a self-report questionnaire validated for the assessment of work productivity and absenteeism in patients with MDD. Our study objective was to establish a minimal clinically important difference (MCID) for the LEAPS.METHODS: Data from the Canadian Biomarker Integration Network in Depression (CAN-BIND)-1 study was used. CAN-BIND-1 involved participants with MDD treated with escitalopram for 8 weeks (n=138). Assessments included the LEAPS, Sheehan Disability Scale (SDS), and Quick Inventory of Depressive Symptomatology-Self-Rated (QIDS-SR). The MCID for the LEAPS was calculated using both distribution (value equivalent to half the standard deviation for LEAPS baseline scores) and anchor-based (comparing to the SDS work item) methods. RESULTS: The LEAPS total score was significantly correlated (p<0.01) with other measures including SDS work item (r=0.598), SDS total score (r=0.609), and QIDS-SR (r=0.678). Using the distribution-based method, the MCID value for LEAPS total score was 3.0 and MCID for LEAPS productivity subscale score was 1.5. Using the anchor-based method, the MCID value for the LEAPS total score was shown to lie between 2.7 and 3.5 and for the LEAPS productivity subscale score between 0.9 and 1.4. CONCLUSIONS: Establishing an MCID is important for determining clinically relevant change in the LEAPS, both for treatment studies and for individual patients in clinical care. For clinical use, the proposed MCID is 3 points for the LEAPS total score and 2 points for the LEAPS productivity subscale score.
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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 | 0.006 | 0.013 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".