Prescribing practice of pregabalin/gabapentin in pain therapy: an evaluation of German claim data
Bibliographic record
Abstract
OBJECTIVES: To analyse the prevalence and incidence of pregabalin and gabapentin (P/G) prescriptions, typical therapeutic uses of P/G with special attention to pain-related diagnoses and discontinuation rates. DESIGN: Secondary data analysis. SETTING: Primary and secondary care in Germany. PARTICIPANTS: Four million patients in the years 2009-2015 (anonymous health insurance data). INTERVENTION: None. PRIMARY AND SECONDARY OUTCOME MEASURES: P/G prescribing rates, P/G prescribing rates associated with pain therapy, analysis of pain-related diagnoses leading to new P/G prescriptions and the discontinuation rate of P/G. RESULTS: In 2015, 1.6% of insured persons received P/G prescriptions. Among the patients with pain first treated with P/G, as few as 25.7% were diagnosed with a typical neuropathic pain disorder. The remaining 74.3% had either not received a diagnosis of neuropathic pain or showed a neuropathic component that was pathophysiologically conceivable but did not support the prescription of P/G. High discontinuation rates were observed (85%). Among the patients who had discontinued the drug, 61.1% did not receive follow-up prescriptions within 2 years. CONCLUSION: The results show that P/G is widely prescribed in cases of chronic pain irrespective of neuropathic pain diagnoses. The high discontinuation rate indicates a lack of therapeutic benefits and/or the occurrence of adverse effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".