The integral inventory for depression, a new, self-rated clinimetric instrument for the emotional and painful dimensions in major depressive disorder
Bibliographic record
Abstract
OBJECTIVE: To assess the reliability and validity of the Integral Inventory for Depression (IID) scale using post hoc analyses of data from a multi-country study (ClinicalTrials.gov: NCT00561509) of patients with major depressive disorder (MDD). METHODS: Patients (N = 1629) completed the IID (comprising two separate dimensions for emotional and physically painful symptoms; maximum score of 65) and a reference scale (16-item Quick Inventory of Depressive Symptomatology Self-Report) at baseline and at follow-up (8 and 24 weeks). Physicians rated MDD symptoms using the Clinical Global Impressions of Severity scale at each visit. Inter-item correlation, internal consistency, external validity, factor structure, and exploratory analysis of an optimal severity cut-off point were assessed. RESULTS: The IID displayed two distinct dimensions (i.e. painful and emotional) with little item redundancy and good internal consistency (Cronbach's α > 0.83 at each visit). The IID displayed good external validity (Pearson's correlations coefficients >0.60 at each visit) and statistically significant agreement (McNemar's test; P < 0.001 at follow-up) with the reference scale. Results suggest that a cut-off score of ≤24 had adequate precision (>80%) to identify patients with and without moderate MDD. CONCLUSIONS: Results suggest that the IID may be a reliable and valid tool for assessing emotional and painful symptoms of MDD.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".