Chronic Noncancer Pain and the Long Term Utility of Opioids
Bibliographic record
Abstract
OBJECTIVE: To report on a long term experience in treating patients with chronic noncancer pain (CNCP). METHODS: One hundred two patients with CNCP were seen every three months and followed for one year or more (median eight years, range one to 22). Demographic data, diagnostic categories and response to therapies were recorded. The utility and safety of opioid therapy, adverse events, impact on disability and issues related to previous psychiatric or chemical dependency history were documented. RESULTS: Most patients reported a variety of neuropathic pain problems and most required chronic opioid therapy after the failure of other treatments. Although 44% reported being satisfied with pain relief despite adverse events, it is noteworthy that the remaining patients chose to continue therapy for the modest benefit of pain relief despite adverse events. Moreover, 54% were less disabled on opioid therapy. CONCLUSIONS: This is a large sample of CNCP patients, most taking opioids over a long period of time. CNCP can be treated by opioids safely and with a modest effect, with improvement in functioning in some patients who are refractory to other measures. If care is taken, opioids may even be used effectively for patients with a history of chemical dependency.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".