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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".