Bibliographic record
Abstract
The opioid system may play a role in some symptoms of major depressive disorder (MDD). We hypothesized that depressed individuals would exhibit an altered response to the agonist HM compared to controls due to an altered function of opioid pathways. In a double-blind, placebo-controlled, randomized study, the effects of HM (6mg, oral) were measured before and 30, 60, 90, 120, 180 and 240 min. after HM intake. Patients with MDD (N=39; HM: 17; placebo: 22) were compared to controls (N=22; HM: 9; placebo: 13) using well-validated scales (e.g. Addiction Research Center Inventory), and psychomotor tasks (e.g. Tracking Test). HM produced time-dependent changes in subjective effects (e.g. VAS-Liking, ARCI-PCAG). Severely depressed (HAMD>24) subjects receiving HM showed significant improvement on negative symptoms compared to controls: differences in the ARCI negative effects composite scale in both peak - baseline and baseline-corrected area-under-the-curve (AUC) mean scores of subjective HM effects were significantly reduced in severely depressed vs. controls (peak - baseline: 96.5 vs. 201.1, p=0.05; AUC: 128.8 vs. 358.2, respectively, p=0.05). Tracking Test mean baseline-corrected % time over the road AUC scores were 10.4 for the severely depressed vs. −13.4 for moderately depressed (p<0.05) and vs. −0.02 for controls (NS). These findings suggest involvement of opioids in the neurobiology of depression. Clinical Pharmacology & Therapeutics (2004) 75, P3–P3; doi: 10.1016/j.clpt.2003.11.009
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".