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Record W3097540676 · doi:10.1055/s-0040-1717647

C-Arm Positionsbestimmung aus einem Röntgenbild mittels Deep Learning

2020· article· de· W3097540676 on OpenAlexaff
Sebastian Andreß, Hooman Esfandiari, Maternus Herold, Wolfgang Böcker, A J Hodgson, Simon Weidert

Bibliographic record

VenueZeitschrift für Orthopädie und Unfallchirurgie · 2020
Typearticle
Languagede
FieldEngineering
TopicRobotics and Sensor-Based Localization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineComputer scienceGynecologyPhysics

Abstract

fetched live from OpenAlex

Fragestellung: Viele chirurgische Eingriffe in der Unfallchirurgie und Orthopädie werden Fluoroskopie-gesteuert durchgeführt. Die Darstellung der anatomischen Leitstrukturen ist jedoch häufig ein zeit- und strahlenintensiver Faktor. Ausgehend von einem primären Röntgenbild wird sich dem angestrebten Strahlenfenster durch wiederholtes Röntgen angenähert.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.880
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.003

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 head0.015
GPT teacher head0.250
Teacher spread0.236 · 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; both teacher heads agree on what is shown here.

Study designSimulation or modeling
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
Published2020
Admission routes1
Has abstractyes

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