Reduced length of stay and hospitalization costs among inpatient hysterectomy patients with postoperative pain management including IV versus oral acetaminophen
Bibliographic record
Abstract
OBJECTIVE: To compare the outcomes of hysterectomy patients who received standard pain management including IV acetaminophen (IV APAP) versus oral APAP. METHODS: We performed a retrospective analysis of the Premier Database (January 2012 to September 2015) comparing hysterectomy patients who received postoperative pain management including IV APAP to those who received oral APAP starting on the day of surgery and continuing up to the third post-operative day, with no exclusions based on additional pain management. We compared the groups on length of stay (LOS), hospitalization costs, and average daily morphine equivalent dose (MED). The quarterly rate of IV APAP use for all hospitalizations by hospital was used as an instrumental variable in two-stage least squares regressions also adjusting for patient demographics, clinical risk factors, and hospital characteristics. RESULTS: We identified 22,828 hysterectomy patients including 14,811 (65%) who had received IV APAP. Study subjects averaged 50 and 52 years of age, respectively in the IV APAP and oral APAP cohorts and were predominantly non-Hispanic Caucasians (≥60% in both cohorts). Instrumental variable models found IV APAP associated with 0.8 days shorter hospitalization (95% CI: -0.92 to -0.68, p<0.0001) and $2,449 lower hospitalization costs (95% CI: -$2,902 to -$1,996, p<0.0001). Average daily MED trended lower without statistical significance (-1.41 mg, 95% CI: -3.43 mg to 0.61 mg, p = 0.17). CONCLUSIONS: Compared to oral APAP, managing post-hysterectomy pain with IV APAP is associated with shorter LOS and lower total hospitalization costs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".