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The Updated ITAG Spinal Simulator (ISS) For Difficult Lumbar Punctures

2024· article· en· W4405488745 on OpenAlexaff
Robert Harutyunyan, Éric Pelletier, Sean Jeffries, Dan Hosi, Harsh Patel, Yajiv Luckheenarain, Thomas M. Hemmerling

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceSimulationLumbarPhysical medicine and rehabilitationMedicineSurgery

Abstract

fetched live from OpenAlex

Lumbar punctures are important and delicate procedures necessitating precise training tools for skill acquisition. This study introduces an advanced simulator with enhanced features including dynamic motor systems and an intuitive GUI. The results from motor accuracy tests indicate significant improvements in precision and reliability, crucial for replicating the nuanced conditions of lumbar puncture procedures. An average absolute angular deviation of 0.5 degrees was observed over 19 trials indicating a consistent performance in mimicking real life anatomical vertebral settings. These advancements set a new standard in medical simulation technology for lumbar punctures, aiming to better prepare healthcare professionals for real-world clinical scenarios. This sets the board for future integrations using augmented and virtual reality paired with motorized systems to facilitate unique and complex training scenarios.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.028
GPT teacher head0.341
Teacher spread0.313 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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