Concomitant use of benzodiazepines in chronic pain patients adherent to extended-release tapentadol or oxycodone treatment—A retrospective claims analysis
Bibliographic record
Abstract
OBJECTIVE: To compare concomitant benzodiazepine (BZDs) use among chronic pain patients adherent to extended-release tapentadol (TapER) or oxycodone (OxnER) and estimate the number of lives potentially saved by switching pa-tients to the less BZD coprescribed treatment. DESIGN: Retrospective database study. SETTING: Patients were identified using the IBM MarketScan® Commercial Database. The opioid overdose death esti-mates were obtained from the US national mortality register and were used to estimate the number of lives potentially saved by switching patients to the opioid treatment with lower rates of BZD coprescribing. PATIENTS, PARTICIPANTS: The authors identified 30,213 chronic pain patients between October 2012 and March 2016. Af-ter propensity score matching, N = 2,355 and N = 6,761 patients were adherent (proportion of days covered ≥80 percent) to TapER and OxnER, respectively. INTERVENTIONS: TapER versus OxnER, during the 180-day treatment. MAIN OUTCOME MEASURE(S): Proportions of BZD coprescribing, BZD dosing patterns in matched patients, and the esti-mated number of lives potentially saved by the opioid treatment switch. RESULTS: TapER patients were less coprescribed BZDs during the treatment period (38.9 percent versus 49.2 percent, OR = 0.659, p < 0.001), and had fewer days of BZD supply per patient (mean: 49.6 versus 70.2 days, p < 0.001) with similar BZD average daily dose. Due to less frequent coprescribing of BZDs with TapER, it is estimated that ~800 deaths may have been avoided in the U.S. as a result of switching patients from OxnER to TapER. CONCLUSIONS: Among treatment-adherent patients, TapER patients had fewer BZD coprescriptions than OxnER pa-tients had. Moreover, when BZDs were coprescribed, those BZD prescriptions were for shorter periods of time. Pro-spective studies are warranted to explore rates and consequences of BZD coprescribing among opioids.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".