Evaluation of Simultaneous Effect of Lovastatin Plus Fluoxetine on Depression Using Linear Mixed Model with LASSO Penalty
Bibliographic record
Abstract
The effect of lovastatin plus fluoxetine on depression has been investigated in many studies, but ignoring other effective factors has decreased the accuracy of the results. The aim of this study was to assess the simultaneous effect of lovastatin plus fluoxetine on depression while controlling a large number of potential covariates using penalized linear mixed model in a longitudinal study. 60 patients with major depressive disorder according to DSM-IV diagnostic criteria were enrolled. The sample was randomly allocated into fluoxetine (up to 40 mg/day) plus lovastatin (30 mg/day) group and fluoxetine (up to 40 mg/day) plus placebo group. Hamilton depression rating scale was used to measure the depression score at baseline, week 2, and week 6. We used linear mixed model (LMM) with least absolute shrinkage and selection operator (LASSO) penalty. Among 60 patients, 39 (65%) were female with a mean age of 31.93 (9.8) years; 51.7% of the patients were married, a majority (73%) lived in village, and 45% of them had high school education. Both groups showed a significant decrease in depression score using Hamilton Depression scale. However, depression score in the treatment group decreased more than the placebo group (Mean=12.8(SD=6.3) vs. Mean=8.2(SD=4.0), t=3.4, P<.001).The proposed model revealed that in the presence of the other covariates, lovastatin plus fluoxetine could play a key role in the reduction of depression. It was also shown that all of the covariates except blood pressure had a significant effect on depression. Linear mixed model with LASSO penalty revealed that sex, age, education, physical illness had the most significant effect on depression. results demonstrated that the masters’ students were possessed of less spiritual growth, indicating the need for more accurate planning towards improving students’ health-promoting lifestyles. So, it was recommended that more attention be paid to the improvement of health-promoting lifestyles, especially in terms of spiritual growth.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".