Two items on the Hamilton Depression rating scale are effective predictors of remission: comparison of selective serotonin reuptake inhibitors with the combined serotonin/norepinephrine reuptake inhibitor, venlafaxine
Bibliographic record
Abstract
Recent studies have shown that the use of subscales derived from the Hamilton Depression (HAM-D) rating scale are just as reliable and enhance sensitivity for detecting response and remission after antidepressant treatment. The purpose of the present study was to determine if the responses on two items of the HAM-D scale, Depressed Mood (item 1) and Psychic Anxiety (item 10), were predictive of remission of depression in placebo-controlled studies following treatment with venlafaxine or selective serotonin reuptake inhibitors (SSRIs). Data from eight active- and placebo-controlled studies consisting of 2027 subjects who met the DSM-III-R/-IV criteria for major depressive disorder were analysed. Three treatment groups were compared: venlafaxine (n =843), SSRI (either fluoxetine, paroxetine or fluvoxamine, n=743) and placebo (n=441). Treatment duration was 6-8 weeks. Patients who scored zero on the depressed mood and the psychic anxiety items of the HAM-D17 scale were designated as responders. These two scores were also combined to create an Absence of Depressive and Anxious Mood (ADAM) score. Between-group rate comparisons in outcome measures were carried out using Fisher's exact test and logistic regression models. Venlafaxine treatment improved depressed mood, psychic anxiety and ADAM scores after 2 weeks with greater efficacy than treatment with SSRIs or placebo. ADAM scores could also predict the odds ratio of a patient achieving a clinical remission (defined as total HAM-D17 score </= 7). The present results demonstrate that using just two items of the HAM-D17 can be very useful in assessing treatment response, differentiating between treatment groups and predicting remission rates.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".