Comparing the Effects of Low-Dose Ketamine, Fentanyl, and Morphine on Hemorrhagic Tolerance and Analgesia in Humans
Bibliographic record
Abstract
Hemorrhage is a leading cause of preventable battlefield and civilian trauma deaths. Ketamine, fentanyl, and morphine are recommended analgesics for use in the prehospital (i.e., field) setting to reduce pain. However, it is unknown whether any of these analgesics reduce hemorrhagic tolerance in humans. We tested the hypothesis that fentanyl (75 µg) and morphine (5 mg), but not ketamine (20 mg), would reduce tolerance to simulated hemorrhage in conscious humans. Each of the three analgesics was evaluated independently among different cohorts of healthy adults in a randomized, crossover (within drug/placebo comparison), placebo-controlled fashion using doses derived from the Tactical Combat Casualty Care Guidelines for Medical Personnel. One minute after an intravenous infusion of the analgesic or placebo (saline), we employed a pre-syncopal limited progressive lower-body negative pressure (LBNP) protocol to determine hemorrhagic tolerance. Hemorrhagic tolerance was quantified as a cumulative stress index (CSI), which is the sum of products of the LBNP and the duration (e.g., [40 mmHg x 3 min] + [50 mmHg x 3 min] …). Compared with ketamine (p = 0.002 post hoc result) and fentanyl (p = 0.02 post hoc result), morphine reduced the CSI (ketamine (n = 30): 99 [73–139], fentanyl (n = 28): 95 [68–130], morphine (n = 30): 62 [35–85]; values expressed as a % of the respective placebo trial’s CSI; median [IQR]; Kruskal-Wallis test p = 0.002). Morphine-induced reductions in tolerance to central hypovolemia were not well explained by a prediction model including biological sex, body mass, and age (R2=0.05, p = 0.74). These experimental data demonstrate that morphine reduces tolerance to simulated hemorrhage while fentanyl and ketamine do not affect tolerance. Thus, these laboratory-based data, captured via simulated hemorrhage, suggest that morphine should not be used for a hemorrhaging individual in the prehospital setting.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".