Computed tomographic measurement of gluteal subcutaneous fat thickness in reference to failure of gluteal intramuscular injections.
Bibliographic record
Abstract
OBJECTIVE: Casual observation of gluteal region fat thickness on computed tomography (CT) of the pelvis leads to the hypothesis that, in some individuals, intended intramuscular injections are not properly deposited in the gluteal muscle. We gathered and analyzed data to determine whether this hypothesis was true. METHODS: CT scans of the pelvis were analyzed over an 18-day period in the fall of 2005. The thickness of gluteal region subcutaneous fat was measured in a standardized manner. RESULTS: Measurement of gluteal region subcutaneous fat thickness was performed for 298 pelvic CT scans. There were 150 male subjects and 148 female subjects. The average gluteal fat thickness for female subjects was 33.2 mm, whereas the average for male subjects was 23.1 mm. Analysis revealed a significant difference in gluteal region fat thickness between male and female subjects. A 37-mm needle, allowing for 6-mm penetration of the gluteal muscle, would not have entered the gluteal muscle fibres in 81 of 148 female subjects (54.7%), in 21 of 150 male subjects (14%), and in 102 of the 298 total sample (34.2%). Analysis revealed a significant difference between male and female subjects with regard to gluteal muscle needle penetration. CONCLUSION AND RECOMMENDATION: An overall predicted failure rate of 34% was identified for intended gluteal intramuscular injections when the standard technique was used. This is important information for care providers who inject medications in the gluteal region. In a significant number of patients, the medications will be injected subcutaneously and not into the gluteal musculature, possibly altering the pharmacokinetics of the administered medication. An alternative injection site should probably be chosen to increase the success rate of intramuscular deposition of medications and vaccines in unselected adults.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".