Analgesic Efficacy of Multiple Single-Shot Peripheral Nerve Blocks on Postoperative Short-Term Opioid Usage and Clinical Outcomes in a Suburban Hospital Setting
Bibliographic record
Abstract
Background: Preoperative single-shot peripheral nerve blocks (sPNBs) represent promising candidates for controlling postoperative pain, reducing dependence on opioid medications, and reducing postoperative constipation and ileus. However, there is not yet complete consensus regarding their efficacy. The primary aim of this study was to assess the impact of various sPNBs on patient short-term opioid demands and pain management parameters. Methods: This single-center study retrospectively reviewed a cohort of 94 adult, elective surgery inpatients (ASA physical status I-III) scheduled for different operations. Sixty-four (68.1%) were selected for sPNB administration (group 1) and compared to the untreated group (group 0) for different clinical parameters. Results: Contrary to the starting hypothesis, a higher proportion of group 1 patients experienced increasing pain intensities during the immediate postoperative period (P < 0.05, Fisher’s exact test), while requiring more bowel care medications (P < 0.05, ? 2 test). Multiple linear regression modeling, however, showed that recovery time positively correlated with the opioid amount consumed (P < 0.01). Although limited, the results obtained in this study do not support an analgesic efficacy for sPNBs. Conclusion: In conclusion, even though our data must be viewed within the limitations of our retrospective study and small group size, we did not find any compelling evidence for the efficacy of sPNB administration in the perioperative period. J Clin Med Res. 2022;14(6):219-228 doi: https://doi.org/10.14740/jocmr4731
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".