Bibliographic record
Abstract
To define treatment response in depression as at least a 50% reduction in total symptom severity is to accept that up to half of patients will continue to have residual symptoms, most commonly low mood/loss of interest, cognitive problems, lack of energy, and difficulty sleeping. In fact, patients' goals for treatment are to return to premorbid levels of functioning. This highlights the importance of assessing both functional outcomes and symptom improvement when evaluating the efficacy of antidepressant medication. Not all patients who achieve symptomatic response/remission will achieve a functional response/remission. In two studies (one with agomelatine and one with escitalopram), 54% of patients receiving agomelatine and 47% of those receiving escitalopram achieved a symptomatic response, and 53% of patients in each study achieved a functional response. However, 42% of patients receiving agomelatine and 35% of those receiving escitalopram had both a symptomatic and a functional response. The four symptoms of depression with the most marked effect on function are sad mood, impaired concentration, fatigue, and loss of interest. Low energy is particularly associated with poor occupational functioning, highlighting the importance of ongoing assessment of patients with depression, focusing particular attention on the symptoms that affect their ability to function, such as fatigue. Depending on the type of residual symptoms, some patients may benefit from combination therapy, such as adding dopamine modulator therapy. Antidepressant therapy is only effective if patients continue to take their medication, and high rates of early discontinuation have been reported. Therefore, when selecting treatment for depression, physicians can maximize the likelihood of adherence and persistence by taking into account both the antidepressant efficacy of treatment, its adverse effects and acceptability to patients.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".