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Record W4313905144 · doi:10.35493/medu.40.14

Robotics in Spinal Surgery

2021· article· en· W4313905144 on OpenAlexvenueno aff
Justin Phung

Bibliographic record

VenueThe Meducator · 2021
Typearticle
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsRoboticsSpinal surgeryRobotic surgeryArtificial intelligenceInstrumentation (computer programming)MedicineMedical physicsComputer scienceRobotSurgery

Abstract

fetched live from OpenAlex

Surgical robotics have been introduced in a number of disciplines, with the aim of minimizing tissue disruption, reducing operating personnel radiation exposure, and improving dexterity and efficiency relative to human operation. In spinal surgery, robotic systems are relatively novel, applied to date largely for the placement of pedicle screw instrumentation. Only a few robotic systems have been approved for spinal surgery, and there remain significant barriers to the widespread implementation of surgical robotic techniques. This review provides an overview of robotic systems in spinal surgery and identifies current limitations that must be addressed before clinical use, including clinical merit relative to freehand navigation systems, steep learning curves, and unclear cost-effectiveness.

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 score0.370
Threshold uncertainty score0.240

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 head0.039
GPT teacher head0.336
Teacher spread0.297 · 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
Published2021
Admission routes1
Has abstractyes

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