Medical resource use and costs among pain patients with potential opioid-tolerability issues
Bibliographic record
Abstract
OBJECTIVE: To estimate excess medical resource use and costs associated with prescription opioid (RxO) tolerability issues. DESIGN: This was an observational, retrospective analysis of deidentified administrative claims data. SETTING: The study included commercially insured patients treated in different healthcare settings captured in the Truven MarketScan claims database. PATIENTS: Patients aged 18-64 years initiating treatment with an RxO (index) and continuously treated with pain relievers over a 6-month period were selected. "Switchers" were patients who discontinued their index RxO and switched to non-RxO pain relievers < 30 days post-index, and whose last pain reliever in the 6-month follow-up period was not an RxO. Such switching was considered a proxy for RxO-tolerability issues. "Continuous RxO users" were patients who remained on the index RxO for the follow-up period. Switchers and continuous RxO users were matched 1:1 on propensity score, baseline medical costs, index RxO days supply, and short-/long-acting index RxO. MAIN OUTCOME MEASURES: Six-month follow-up medical resource use and costs were compared between matched switchers and continuous RxO users. RESULTS: A total of 10,704 pairs of switchers and continuous RxO users were matched. In the 6-month follow-up period, switchers had more outpatient (7.5 vs 6.8; p < 0.001) and inpatient (0.05 vs 0.04; p = 0.002) visits and longer inpatient stays (0.26 days vs 0.19; p = 0.006) compared to continuous RxO users. Switchers also had higher total medical costs ($4,522 vs $3,657; p < 0.001). CONCLUSIONS: Switchers incur greater medical resource use and costs than similar patients continuously treated with their index RxO.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".