Pain-Prescription Differences - An Analysis of 500,000 Discharge Summaries
Bibliographic record
Abstract
BACKGROUND: Pain-relief prescriptions have led to an alarming increase in drug-related abuse. OBJECTIVE: In this study, we estimate the pain reliever prescription rates at a major German academic hospital center and compare with the nationwide trends from Germany and prescription reports from the USA. METHODS: We analysed >500,000 discharge summaries from Charité, encompassing the years 2006 to 2015, and extracted the medications and diagnoses from each discharge summary. Prescription reports from the USA and Germany were collected and compared with the trends at Charité to identify the frequently prescribed pain relievers and their world-wide utilization trends. The average costs of pain therapy were also calculated and compared between the three regions. RESULTS: Metamizole (dipyrone), a non-opioid analgesic, was the most commonly prescribed pain reliever at Charité (59%) and in Germany (23%) while oxycodone (29%), a semi-synthetic opioid, was most commonly ordered in the USA. Surprisingly, metamizole was prescribed to nearly 20% of all patients at Charité, a drug that has been banned for safety reasons (agranulocytosis) in most developed countries including Canada, United Kingdom, and USA. A large number of prospective cases with high risk for agranulocytosis and other side effects were found. The average cost of pain therapy greatly varied between the USA (125.3 EUR) and Charité (17.2 EUR). CONCLUSION: The choice of pain relievers varies regionally and is often in disagreement with approved indications and regulatory guidelines. A pronounced East-West gradient was observed with metamizole use and the opposite with prescription opioids.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".