Pregabalin for chronic pain: does one medication fit all?
Bibliographic record
Abstract
BACKGROUND: Pregabalin is frequently prescribed for chronic non-cancer pain. No previous study has examined its off-label use. OBJECTIVES: Our primary aim was to assess the proportion of patients taking pregabalin for conditions approved by Health Canada ('on-label') and compare their perspectives on its use to those who use pregabalin for other conditions ('off-label'). METHODS: Patients who have used pregabalin within the past year were recruited from two registries of chronic non-cancer pain patients treated in tertiary care clinics: the Quebec Pain Registry and the Fibromyalgia Patients Registry. Data on the use of pregabalin and its perceived benefits were collected from the registries and from completed questionnaires. RESULTS: Out of 4339 screened chronic non-cancer pain patients, 355 (8.18%) met the study selection criteria. Three-quarters of them (268/355) used pregabalin for pain conditions not approved by Health Canada and were therefore regarded as off-label users. The most prevalent condition for pregabalin use was lumbar back pain (103/357; 28.85%). There were no significant differences between on- and off-label users in their perceived satisfaction from pregabalin therapy and its effect on function and quality of life. Among former users, the most prevalent reason for discontinuation was adverse effects, mainly dry mouth and weight gain. CONCLUSIONS: We conclude that despite specific indications for pregabalin prescription, it is mainly used off-label, notably for low back pain. Nevertheless, off-label users were equally satisfied with its clinical effects. Although formal exploration of the broader analgesic properties of pregabalin is warranted, treating heterogeneous chronic pain conditions with pregabalin may be legitimate. LIMITATIONS: The main limitations of the study are patients' low response rate, the recruitment of participants solely from a tertiary pain center and not from the general patient population and a possible recall bias that may have arisen from the retrospective nature of the study.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".