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Record W4315750311 · doi:10.1101/2023.01.09.23284320

Quantitative Assessment of Injectable Medication Delivery Practices

2023· preprint· en· W4315750311 on OpenAlexafffund
Salena Aggerwal, Amir Minerbi, Lt Peter Beliveau, LCol Sean Meredith, MCpl Sasha Lalonde, Erica Laurin, Gaurav Gupta

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsCanadian Armed ForcesUniversity of Ottawa
FundersUniversity of British Columbia
KeywordsMedicineMedical emergencyPatient careEmergency medicineProtocol (science)NursingAlternative medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.056
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.122
GPT teacher head0.434
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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