Intrathecal opioid treatment for chronic non-malignant pain: a 3-year prospective study
Bibliographic record
Abstract
Intrathecal (IT) opioid therapy is a treatment alternative for patients with severe chronic non-malignant pain. Several uncontrolled retrospective and prospective outcome studies have suggested a benefit in chronic non-malignant pain patients, but uncertainties about patient selection in these studies weaken the results. This study evaluated long-term outcome of IT opioid therapy in chronic non-malignant pain prospectively, and included two comparative groups to improve understanding of selection criteria and relative severity of intrathecal pump recipients (PRs). The study subjects included 38 PRs while the comparative groups included 31 intrathecal candidates who either had an unsuccessful trial, or declined the IT therapy, and another group of 41 newly referred patients. The following data were analyzed at study entry, and at 6 monthly intervals for a 3-year period: Symptom Check List 90 (SLC-90), SF-36 Health survey, Beck Depression Inventory, McGill Pain Questionnaire (short form), Oswestry Disability Index, Pain Drawings and Pain rating on visual analogue scale. Data analysis suggests the study group of PRs had improvements in pain, mood, and function from baseline to 36 months. These same parameters improved among new referrals (less severe patients receiving conservative pain management) while non-recipients significantly worsened. Although PRs improved, they were still worse off at 36 months than new referrals were at baseline. The study showed that when patients with extremely severe pain problems are selected as pump candidates, they will likely improve with the therapy, but their overall severity of pain and symptoms still remains high.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".