Liposomal Bupivacaine for Fascial Plane Block: Reply
Bibliographic record
Abstract
We thank Dr. Pace1 for selecting our publication2 highlighting the lack of difference between liposomal and plain bupivacaine, and presenting alternative findings that are based on the differences between the frequentist and Bayesian approaches for meta-analyses. We present in this reply a few of our thoughts on the different analyses.First, while we have limited awareness of the use of the Bayesian method as a mainstream approach in meta-analyses, we are familiar with the nascent3,4 Bayesian statistics that are slowly gaining popularity as an alternative analytic method.Second, for acute pain meta-analyses comparing two different interventions where the mean of a group of data (i.e., randomized trials) is most important, a frequentist analysis may be better suited for the clinician. In contrast, a Bayesian model may be more applicable for those clinicians wishing to evaluate the probability that a certain intervention will be better for an outcome. On a broad scale, clinicians assessing the results of a single outcome (e.g., rest pain at 24 h) may be more interested in knowing whether one treatment is “better” than another treatment, rather than the “probability” of being better; this is more consistent with a frequentist approach.Third, we would caution readers against an over-reliance on P values for interpreting clinical research. While the threshold for statistical significance does provide some insights into the results of a clinical research question, there should always be interpretation based on clinical importance and the minimal clinically important difference. For even if we utilize the Bayesian model to accomplish a statically significant 95% credible interval of –0.43 cm.h to –0.01 cm.h, this alternative result is still not clinically important, especially when the clinical importance threshold is 3.0 cm.h. Indeed, the differences between the results of Bayesian and frequentist approaches were trivial, and the effect size of liposomal bupivacaine was very small.Finally, interpretation of research findings is contextual. Using the frequentist method, we have already demonstrated that liposomal bupivacaine is not superior to plain bupivacaine when used in perineural nerve blocks,5 periarticular infiltration,6 and surgical field infiltration.7 Our current findings in the setting of fascial plane blocks are consistent, and the alternative Bayesian analysis does not alter this finding in a clinically meaningful way.The authors declare no competing interests.
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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 | 0.009 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.038 | 0.045 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".