Use of intra-articular lidocaine as analgesia in anterior shoulder dislocation: a review and meta-analysis of the literature.
Bibliographic record
Abstract
INTRODUCTION: The shoulder joint is the most commonly injured major joint in patients who present to the hospital emergency department today. In the community the incidence of shoulder joint injuries is 11.2 cases per 100,000 person-years. Traditionally, procedural sedation and analgesia (PSA) has been used to facilitate the reduction of anterior shoulder dislocations. However, there are risks of complication, such as respiratory depression, particularly in certain populations. As such, the use of intra-articular lidocaine (IAL) has been suggested as an alternative method of analgesia. METHODS: We searched EMBASE (Ovid) and MEDLINE (PubMed) databases using the keywords "shoulder, dislocation, and/or reduction" from the respective start dates of the databases until October 2008. RESULTS: Based on the current literature, it appears that the IAL method provides, at a minimum, the same level of pain control and reduction success as the procedural sedation method, while markedly reducing the time spent by the patient in the emergency department and the cost of treatment. The likelihood of complications is arguably less with the use of IAL. CONCLUSION: Although more research is this area is merited, physicians may consider IAL as an alternative to PSA in the management of anterior shoulder dislocations.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".