Diclofenac--Acetaminophen Combination Induced Acute Kidney Injury In Postoperative Pain Relief
Bibliographic record
Abstract
PURPOSE: The objective of this study was to determine: 1) the incidence and the risk factors of diclofenac/acetaminophen combination as a single agent induced Acute Kidney Injury (AKI) in postoperative pain relief 2) the average cost and length of hospital stay for patients in AKI group and non-AKI group. METHODS: All patients with no prior history of chronic kidney disease (CKD) and normal serum creatinine [44~130 μmol /l] who received diclofenac and acetaminophen combination as a single agent intramuscularly (IM) between January and December 2015 in The Second Xiangya Hospital, Changsha, Hunan, China were included in this retrospective own-control study. Baseline serum creatinine (SCr) and SCr during NSAID use were collected. AKI is defined as an increased of Scr over 1.5 times the baseline. Multivariate analyses were performed with a logistic regression model to assess the significant risk factors of AKI. RESULTS: A total of 821 patients were included in the study with 63 [7.7%] patients had diclofenac/acetaminophen combination single agent induced AKI. Multivariate analysis confirmed that using diclofenac/acetaminophen combination after surgeries within 24 h were significantly associated with AKI [odds ratio, OR, 2.173; 95% CI, 1.113-4.243; P=0.023]. The average cost and length of hospitalization in AKI group was 1.87 times [p=0.000] and 1.2 times [p=0.043] comparison than non-AKI group, respectively. CONCLUSIONS: The incidence of diclofenac/acetaminophen combination single agent induced AKI in postoperative pain relief was 7.7%. Patients with hypertension or liver cirrhosis was more likely to develop AKI and using diclofenac/acetaminophen combination after surgeries within 24 h was significant risk factors for AKI. AKI prolonged the cost and length of hospitalization. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".