Bilateral Intravenous Regional Anesthesia
Bibliographic record
Abstract
BACKGROUND: Ketorolac, when added to lidocaine, has been shown to reduce early tourniquet pain during intravenous regional anesthesia (i.v.RA) in patients. Although the effectiveness of ropivacaine 0.2% for i.v.RA is equal to that of lidocaine 0.5% but significantly reduces central nervous system side effects after release of the tourniquet, it provides no advantage with regard to tourniquet tolerance times. Simultaneous bilateral i.v.RA with ropivacaine 0.2% was used to test the hypothesis that ketorolac modifies tourniquet tolerance and to test whether drug combinations can be evaluated in one study session. METHODS: Ten healthy, unsedated volunteers received 30 ml of ropivacaine 0.2% in each upper arm with 2 ml of normal saline in one arm and 30 mg of ketorolac in the contralateral arm for i.v.RA. Both proximal tourniquets remained inflated for 30 min, followed by inflation of the distal tourniquets and release of the proximal ones. Verbal numeric scores for tourniquet pain were recorded for both extremities. Central nervous system side effects were graded after release of each distal tourniquet. RESULTS: There was no difference between the two upper extremities with regard to surgical anesthesia and tourniquet tolerance. Total tourniquet tolerance was a median of 58.5 min (range, 45-90 min) and 60.5 min (39-79 min) in the normal saline and ketorolac groups, respectively. After release of the distal tourniquets, 5 of 10 volunteers experienced mild dizziness. CONCLUSIONS: The addition of ketorolac to ropivacaine does not improve tourniquet tolerance. Minimal central nervous system side effects after tourniquet release suggest that a total of 60 ml ropivacaine 0.2% for bilateral i.v.RA is a useful model for comparison of i.v.RA drug combinations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".