Antidepressants for pain management in adults with chronic pain: a network meta-analysis
Bibliographic record
Abstract
ObjectivesThis is a protocol for a Cochrane Review (intervention).The objectives are as follows:To assess the comparative e icacy and safety of antidepressants for adults with chronic pain.We will achieve this by:assessing the e icacy of antidepressants by type, class and dose in improving pain, mood, patient global impression of change, physical functioning, sleep quality and quality of life; assessing the number of adverse events of antidepressants by type, class and dose; ranking antidepressants in the e icacy of treating pain, mood and adverse events.Background This is a protocol for a Cochrane Review and network meta-analysis to assess the comparative e icacy and safety of antidepressants for adults with chronic pain. Description of the conditionChronic pain is common in adults internationally, and is defined as pain lasting or recurring for more than three months (IASP 2019).Chronic pain can occur with no tissue damage apparent.Therefore, the definition of chronic pain is split into primary chronic pain and secondary chronic pain.Primary chronic pain is diagnosed when the pain cannot be better explained by another condition, and is characterised by disability and emotional distress (e.g.non-specific low back pain; Treede 2015).Secondary chronic pain is pain that can be attributed to a specific, recognisable cause, and is grouped into the following six categories.Cancer-related pain: pain caused by cancer or treatment, including pain caused by chemotherapy.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.030 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".