Bibliographic record
Abstract
Abstract Chronic pain is widespread and the use of opioids for chronic pain is also common. Frequently benzodiazepines are concomitantly prescribed in these patients, for anxiety, sleep disorders, and muscle pain and spasm. In the United States, Canada, and the European Union, increases in benzodiazepine prescribing has been seen, in some cases over 16% over the last decade. Unfortunately, the combination of opioids and benzodiazepines is correlated with overdose and overdose death. Few data exist to support the use of benzos for sleep, muscle spasm, or the long-term treatment of anxiety in the context of pain. It has been further shown that the use of benzodiazepines carries other adverse events and issues. It is estimated that the elimination of benzodiazepines would decrease overdoses by over 15%. The deprescribing of benzodiazepines should become common practice in the professional pain community and their use drastically limited. The authors suggest an approach to the discontinuation of benzodiazepines that includes extensive patient involvement. Other options for anxiety, sleep disturbances, and muscle relaxation are available and should be considered. For those already on these agents (legacy patients), tapering with the goal of discontinuation in a safe and person-centered process should be undertaken.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 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".