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Record W1748064802 · doi:10.1002/rcs.1568

Forces exerted during microneurosurgery: a cadaver study

2014· article· en· W1748064802 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Medical Robotics and Computer Assisted Surgery · 2014
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of Calgary
FundersImperial College LondonNational Institute for Health and Care ResearchWellcome TrustWellcome
KeywordsCadaverMagnificationDissection (medical)MicrosurgeryRoboticsNeurosurgeryMedicineExtracorporealComputer scienceSurgeryAnatomyPhysical medicine and rehabilitationRobotArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND: A prerequisite for the successful design and use of robots in neurosurgery is knowledge of the forces exerted by surgeons during neurosurgical procedures. The aim of the present cadaver study was to measure the surgical instrument forces exerted during microneurosurgery. METHODS: An experimental apparatus was set up consisting of a platform for human cadaver brains, a Leica microscope to provide illumination and magnification, and a Quanser 6 Degrees-Of-Freedom Telepresence System for tissue manipulation and force measurements. RESULTS: The measured forces varied significantly depending on the region of the brain (P = 0.016) and the maneuver performed (P < 0.0001). Moreover, blunt arachnoid dissection was associated with greater force exertion than sharp dissection (0.22 N vs. 0.03 N; P = 0.001). CONCLUSIONS: The forces necessary to manipulate brain tissue were surprisingly low and varied depending on the anatomical structure being manipulated, and the maneuver performed. Knowledge of such forces could well increase the safety of microsurgery.

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.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.013
GPT teacher head0.241
Teacher spread0.228 · 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