Short-term Ketamine Infusion for Acute Pain in an Opioid-tolerant Patient: a Case Report
Bibliographic record
Abstract
Physicians are increasingly likely to encounter opioid tolerant patients suffering from acute pain. In the opioid tolerant patient, low-dose ketamine can provide safe, effective analgesia. However, hospital policies and unfamiliarity with ketamine have limited its use. We report a case of a patient who was tolerant to opioids as a result of opioid use disorder and who received ketamine by infusion, on a regular hospital ward, as an effective treatment for acute pain. We suggest that an internist or hospitalist can use low dose ketamine by infusion to treat acute, reversible pain in the opioid tolerant patient. Les médecins sont de plus en plus susceptibles de rencontrer des patients tolérants aux opioïdes souffrant de douleurs aiguës. Chez le patient tolérant les opioïdes, la dose faible de kétamine peut fournir une analgésie efficace et sûre. Cependant, les politiques des hôpitaux et la méconnaissance de la kétamine ont limité leur utilisation. Nous rapportons le cas d'un patient qui était tolérant aux opioïdes, qui, à la suite d'un trouble de l'utilisation des opioïdes, a reçu de la kétamine par perfusion, dans un milieu hospitalier régulier, comme traitement efficace pour la douleur aiguë. Nous suggérons qu'un interniste ou practicien hospitalier peut utiliser une dose faible de kétamine par perfusion pour traiter une douleur aiguë et réversible chez le patient tolérant les opioïdes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".