Measuring depressive symptoms in the naturalistic primary-care setting
Bibliographic record
Abstract
BACKGROUND: The majority of individuals with major depressive disorder are diagnosed and treated in the primary-care setting. A quantifiable critical objective in the management of depression is to achieve and sustain full symptomatic remission. The HAMD-7 is a depression metric validated in both tertiary and primary-care settings. METHODS: Herein, we further characterise the psychometric properties of the HAMD-7 in depressed patients treated in primary-care settings. Several cut-scores were evaluated for maximum agreement; diagnostic efficacy statistics with the original HAMD-7 items were also evaluated. We compared performance of the HAMD-7 in primary care to a previously characterised tertiary sample. RESULTS: The depressive symptoms most frequently endorsed (>or=70%) and most sensitive to change during antidepressant treatment in depressed primary-care patients were depressed mood, guilt, work and activities, psychic and somatic anxiety and fatigue. LIMITATIONS: This is a post hoc analysis of a primary-care database; assumptions regarding the definition of symptomatic remission in depression affect interpretation. CONCLUSION: Measurement-based care with the HAMD-7 quantifies the severity of commonly reported depressive items and their responsivity to treatment. The HAMD-7, inclusive of the suicide item, is capable of tracking symptom progress, with a validated remission cut-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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".