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Record W4399393781 · doi:10.1038/s41372-024-02018-x

A prospective evaluation of tibial insertion sites for intraosseous needles to gain vascular access in Asian neonates

2024· article· en· W4399393781 on OpenAlexaff
Chutima Sengasai, Preeyacha Pacharn, Bosco Paes, Ratchada Kitsommart

Bibliographic record

VenueJournal of Perinatology · 2024
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsMcMaster University
FundersFaculty of Medicine Siriraj Hospital, Mahidol UniversityMahidol University
KeywordsMedicineCadaverNuclear medicineProspective cohort studySurgery

Abstract

fetched live from OpenAlex

OBJECTIVES: To determine the appropriate intraosseous (IO) needle insertion site, optimal depth and success using a drill-assisted device (DAD) versus a manually inserted needle (MIN). METHODS: Computed tomography scans of neonatal cadavers were analyzed. Success was based on tibial needle tip placement within the marrow cavity and contrast media distribution. RESULTS: Nineteen cadavers (38 tibiae) were included. The overall success rate was comparable between DAD and MIN needles, but reduced in very-low birthweight (VLBW) infants. The insertion site was consistent across birth weight groups. Contrast leakage occurred overall in 15.8% and 41.7% in VLBW infants and was insignificantly greater in DAD versus MIN needles. Minimum and maximum puncture depth was adjusted for higher BW groups. CONCLUSION: IO needles should be placed 2 cm below and 1-2 cm medial to the tibial tuberosity. MIN needles are preferred to minimize leakage. IO depth should be modified by birth weight.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.036
GPT teacher head0.377
Teacher spread0.341 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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