Reply to the Letter to the Editor: Single-injection or Continuous Femoral Nerve Block for Total Knee Arthroplasty?
Bibliographic record
Abstract
To the editor, We would like to thank Dr. Byrne for his comments regarding our recent publication exploring the effects of a single injection versus two types of continuous infusion for femoral nerve block [1]. We agree that Brodner and colleagues [3] previously have concluded that ropivacaine 0.2% and ropivacaine 0.3% were equivalent in terms of pain outcomes compared to ropivacaine 0.1% when used as continuous infusion for femoral nerve blocks. However, Brodner and colleagues designed their study to test the hypothesis that ropivacaine 0.3% was superior to ropivacaine 0.2% or 0.1%, and the authors’ conclusion that 0.1% was ineffective was based on an interim statistical analysis of only 20 patients. Although their study demonstrated that increasing the concentration of ropivacaine above 0.2% did not confer any advantage, we relied on another study, that of Paauwe et al [4], to address the effect of decreasing the concentration of ropivacaine below 0.2%. Indeed, Paauwe and colleagues [4] found no advantage in using concentrations of ropivacaine less than 0.1%. In designing our protocol, we were also mindful that Brodner and colleagues had not used a comprehensive multimodal analgesia strategy. The lack of benefits associated with increased concentrations of ropivacaine (ie greater than 0.1%) recently has been confirmed in another study [2]. We recognize that we used a lower total mass of local anesthetic for both the priming injection and for the continuous infusion compared to what has been published recently by Spangehl and colleagues [5]. Such differences in postoperative therapeutic regimens may explain the discrepancy in reported pain scores between institutions.
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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 | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.025 | 0.026 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".