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Record W2480030593

DEVELOPING A SPINE SURGERY TRAINING TOOL: MEASURING FORCES IN THE PEDICLE

2018· article· en· W2480030593 on OpenAlexaff
Aubrey Blair-Pattison, Jonn B. Henke, Gregory S. Penny, Richard Hu, Ganesh Swamy, Carolyn Anglin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCadaveric spasmMedicineFixation (population genetics)SurgeryScoliosisHaptic technologyOrthodonticsPhysical medicine and rehabilitationPhysical therapyComputer scienceSimulation
DOInot available

Abstract

fetched live from OpenAlex

Summary Statement Incorrect pedicle screw placement can lead to neurological complications. Practice outside the operating room on realistic bone models, with force feedback, could improve safety. Pedicle forces in cadaveric specimens are reported, to support development of a training tool for residents. Introduction Inserting screws into the vertebral pedicles is a challenging step in spinal fusion and scoliosis surgeries. Errors in placement can lead to neurological complications and poor mechanical fixation. The more experienced the surgeon, the better the accuracy of the screw placement. A physical training system would provide orthopaedic residents with the feel of performing pedicle cannulation before operating on a patient. The proposed system consists of realistic bone models mimicking the geometry and material properties of typical patients, coupled with a force feedback probe. The purpose of the present study was to determine the forces encountered during pedicle probing to aid in the development of this training system. Methods We performed two separate investigations. In the first study, 15 participants (9 expert surgeons, 3 fellows, 3 residents) were asked to press a standard pedicle awl three times onto a mechanical scale, blinded to the force, demonstrating what force they would apply during safe pedicle cannulation and during unsafe cortical breach. In the second study, three experienced surgeons used a standard pedicle awl fitted with a one-degree of freedom load cell to probe selected thoracolumbar vertebrae of eight cadaveric specimens to measure the forces required during pedicle cannulation and deliberate breaching, in randomised order. A total of 42 pedicles were tested. Results Both studies had wide variations in the results, but were in general agreement. Cannulation (safe) forces averaged approximately 90 N (20 lb) whereas breach (unsafe) forces averaged approximately 135–155 N (30–35 lb). The lowest average forces in the cadaveric study were for pedicle cannulation, averaging 86 N (range, 23–125 N), which was significantly lower (p 0.1). Cannulation forces were on average 59% of the breach forces (range, 19–84%) or conversely, breach forces were 70% higher than cannulation forces. Discussion To our knowledge, axial force data have not previously been reported for pedicle cannulation and breaching. A large range of forces was measured, as is experienced clinically. Additional testing is planned with a six-degree-of-freedom load cell to determine all of the forces and moments involved in cannulation and breaching throughout the thoracolumbar spine. These results will inform the development of a realistic bone model as well as a breach prediction algorithm for a physical training system for spine surgery. The opportunity to learn and practice outside of the operating room, including learning from deliberate mistakes, should increase the confidence and comprehension of residents performing the procedure, enhance patient safety, reduce surgical time, and allow faster progression of learning inside the operating room.

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)0.002
metaresearch head score (Gemma)0.005
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.218
GPT teacher head0.284
Teacher spread0.066 · 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
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
Published2018
Admission routes1
Has abstractyes

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