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Record W2605630772 · doi:10.1117/12.2256458

Design of a head mounted optical tracking system for surgical navigation (Conference Presentation)

2017· article· en· W2605630772 on OpenAlexaff
Ryan Deorajh, Peter N. Morcos, Jamil Jivraj, Joel Ramjist, Victor X. D. Yang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceSurgical instrumentMagnificationPresentation (obstetrics)Head and neckTracking (education)SimulationMedical physicsComputer visionMedicineSurgery

Abstract

fetched live from OpenAlex

When using surgical loupes and other head mounted surgical instruments for an extended period of time, many surgeons experience fatigue during the procedure, which results in a lot of pain in the neck and upper back. This is primarily due to the surgeon being subjected to long periods of uncomfortable positions, due to the design of the surgical instrument. To combat this issue, the surgeon is required to have a larger freedom of movement, which will reduce the fatigue in the affected areas, and allow the surgeon to comfortably operate for longer periods of time. The proposed design will incorporate an optical magnification system on a surgical head mounted display that will allow the surgeon to freely move their head and neck during the operation, while the optics are focused on the area of interest. The design will also include an infrared tracking system in order to acquire the field of view data, which will be used to control the optics. The reduction in neck pain will also be quantified using a clinically standardized numeric pain rating scale.

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)0.001
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: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0080.003

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 head0.175
GPT teacher head0.422
Teacher spread0.247 · 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
GenreMethods

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
Published2017
Admission routes1
Has abstractyes

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