Quantitative Assessment of Injectable Medication Delivery Practices
Bibliographic record
Abstract
Abstract Background While medical advances for in-hospital care rapidly evolve, a mainstay of effective pre-hospital care remains the ability to treat medical emergencies such as anaphylaxis, overdosing, and/or uncontrolled bleeding through rapid administration of appropriate medication. Therefore, investigators looked at various injection methods and their possible utility in medical emergencies. Method 30 participants were asked to inject ‘medication’ that mimicked three different methods of injection: 1) autoinjectors, 2) prefilled syringes, and 3) traditional standard syringes using clinical scenarios. Three variables that were measured in the study were: the time required to complete the injection, the perceived difficulties, and the participant’s performance errors. Results The perceived difficulty and injection time for the autoinjector device were statistically significantly lower compared to prefilled syringes and standard syringes. No significant difference in errors were seen between platforms. Discussion To our knowledge, this is the first study to quantify the gain of efficiency when comparing autoinjectors to other methods of medication administration, like prefilled syringes or drawing medication from vials for administration. The clinical implications of the noted differences are not clear at this time. Many potential limitations exist, including the size of the study, the use of non-clinical participants, the immediate use of platforms after training, and the lack of applied stress in the environment. Conclusion This study compares autoinjectors to other methods of medication administration; prefilled syringes and standard syringes. Further study in larger datasets with clinicians and/or military personnel is required to compare these platforms in various environments. The outcome of this project provides insights into the relative efficiencies of treating medical emergencies such as anaphylaxis, overdosing, and/or uncontrolled bleeding.
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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 | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".