Recommendations for the pharmacological treatment of treatment-resistant depression: A systematic review protocol
Bibliographic record
Abstract
Introduction Depression is a serious and widespread mental health disorder. Although effective treatment does exist, a significant proportion of patients with depression fail to respond to antidepressant treatment trials, a condition named treatment-resistant depression. Efficient approach should be given this condition in order to revert the burden caused by depression. Clinical practice guidelines (CPGs) are evidence-based health promotion instruments to improve diagnosis and treatment. CPGs recommendations for treatment-resistant depression must be trustworthy. The objective of the proposed study is to systematically identify, appraise the quality of CPGs for the treatment of depression and elaborate a synthesis of recommendations for treatment-resistant depression of CPGs considered to be of high quality and with high quality recommendations. Methods and analysis We will search the databases of organizations, such as PubMed, Embase, Cochrane Library, PsycInfo, and the Virtual Health Library, and organizations that develop CPGs. Three independent researchers will assess the quality of the CPGs and their recommendations using the AGREE II and AGREE-REX instruments, respectively. Given the identification of divergences and convergences as well as weak and strong points among high quality CPGs, our work may help developers, clinicians and eventually patients. Ethics and dissemination No ethical approval is required for a systematic review, as no patient data will be used. The research results will be disseminated in conferences and submitted to a peer reviewed journal.
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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 | 0.131 | 0.156 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.018 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.081 | 0.012 |
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".