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Record W2924494920 · doi:10.48550/arxiv.1903.10028

Navigated interventions in the head and neck area: standardized assessment of a new handy field generator

2019· preprint· en· W2924494920 on OpenAlexaboutno aff
Benjamin J. Mittmann, Alexander Seitel, Lena Maier‐Hein, Alfred M. Franz

Bibliographic record

VenuearXiv (Cornell University) · 2019
Typepreprint
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsnot available
Fundersnot available
KeywordsRobustness (evolution)JitterSoftware portabilityComputer scienceMetric (unit)Tracking (education)Tracking errorObservational errorSimulationMathematicsStatisticsEngineeringOperations managementTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

Electromagnetic (EM) tracking enables localization of surgical instruments within the magnetic field emitted by an EM field generator (FG). Usually, the larger a FG is, the larger its tracking volume is. However, the company NDI (Northern Digital Inc., Waterloo, ON, Canada) recently introduced the Planar 10-11 FG, which combines a compact construction (97mm x 112mm x 31mm) with a relatively large, cylindrical tracking volume (diameter: 340mm, height: 340mm). Using the standardized assessment protocol of Hummel et al., the FG was tested with regard to its tracking accuracy and to its robustness with respect to external sources of disturbance. The mean positional error (5cm distance metric according to Hummel protocol) was 0.59mm, with a mean jitter of 0.26mm in the standard setup. The mean orientational error was found to be 0.10°. The highest positional error (4.82mm) due to metallic sources of disturbance was caused by the steel SST 303. In contrast, steel SST 416 caused the lowest positional error (0.10mm). Overall, the Planar 10-11 FG tends to achieve better tracking accuracy results compared to other NDI FGs. Due to its compact construction and portability, the FG could contribute to increased clinical use of EM tracking systems.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.234
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

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

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 head0.108
GPT teacher head0.284
Teacher spread0.176 · 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 teacher head, 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
Published2019
Admission routes1
Has abstractyes

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