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Record W48830199 · doi:10.7205/milmed.170.3.251

Cadaver Testing to Validate Design Criteria of an Adult Intraosseous Infusion System

2005· article· en· W48830199 on OpenAlexaff
David L. Johnson, Judy Findlay, Andrew Macnab, Lark Susak

Bibliographic record

VenueMilitary Medicine · 2005
Typearticle
Languageen
FieldMedicine
TopicSurgical site infection prevention
Canadian institutionsChildren's & Women's Health Centre of British ColumbiaD-Wave Systems (Canada)3v Geomatics (Canada)
Fundersnot available
KeywordsCadaverMedicineHypertonic salineSternumSalineSurgeryBiomedical engineeringNuclear medicineAnatomyAnesthesia

Abstract

fetched live from OpenAlex

INTRODUCTION: The FAST 1 intraosseous (IO) infusion system was designed to deliver fluids and medications into the adult sternum in the prehospital and battlefield environments. OBJECTIVE: To test the prototype in 106 cadavers and excised sterna and compare it with other IO devices. RESULTS: The insertion force was similar to that of other IO devices (mean, 8.5 kg; range, 2.3-19.6 kg). In 39 of 39 trials, the depth-control mechanism inserted the portal within 1.0 mm of a predetermined distance below the anterior surface of the cortical bone. If misplaced, underpenetration was more likely than overpenetration (mean displacement, -0.3 mm; SD, 0.5 mm). After release, the portal could not be advanced further into the manubrium. Marrow was accessed in 75 of 77 trials. Mean flow rates were 109 mL/min for normal saline solution and 102 mL/min for hypertonic saline/dextran, similar to the Cook Sur-Fast device. CONCLUSION: The cadaver and bench tests demonstrated the reliability and safety of the FAST 1 system at the design/prototype stage.

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)0.009
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.043
GPT teacher head0.322
Teacher spread0.279 · 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 designBench or experimental
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

Citations18
Published2005
Admission routes1
Has abstractyes

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