Preventing sciatic nerve injury from intramuscular injections: literature review
Bibliographic record
Abstract
BACKGROUND: Injury to the sciatic nerve (SN) is a serious complication of intramuscular injection. AIM: The purpose of this paper was to identify factors associated with such iatrogenic injury in adults and measures that nurses may take to prevent it. METHOD: A review of the English language literature was undertaken to identify applicable research studies and determine the information that currently is being disseminated on relevant injection procedure. Legal databases were also searched for pertinent court decisions. DISCUSSION: The evidence is that injury to the SN is associated with use of the dorsogluteal (DG) site for injection. The choice of site for injection must be based on good clinical judgment, using the best evidence available and individualized assessment of the client. There is wide agreement in the literature that the ventrogluteal site is preferable. If the DG site is chosen, the nurse must have a full appreciation of the anatomy of the site and proximate anatomic structures, be able to accurately identify anatomic landmarks and site boundaries, and administer the injection with meticulous technique. Not only may SN injury resulting from erroneous injection cause client discomfort, morbidity and lasting disability, but it also provides the basis for nursing negligence suits. CONCLUSION: The research base for intramuscular injection is limited. Studies on various aspects of the procedure need to be carried out to provide support for clinical guidelines.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".