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Record W2572646100 · doi:10.1055/s-0035-1554209

A Qualitative Web-Based Expert Opinion Analysis on the Adoption of Intraoperative CT and Navigation Systems in Spine Surgery

2015· article· en· W2572646100 on OpenAlexaffabout
Mélissa Nadeau, Juliet Batke, Charles G. Fisher, John Street

Bibliographic record

VenueGlobal Spine Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineSAFERExpectancy theoryLife expectancyNavigation systemSurgeryMedical physicsArtificial intelligenceComputer sciencePopulationPsychology

Abstract

fetched live from OpenAlex

Introduction Intraoperative computed tomography (CT) and navigation systems have several potential uses in instrumented spine surgery and may decrease patient morbidity and complications leading to very significant cost-effectiveness implications. Despite these proposed advantages, its adaptation in centers across Canada, New Zealand, and Australia is not ubiquitous. The goal of this study is to identify facilitators and barriers to the adoption of intraoperative CT and navigation systems technology by spine surgeons. Materials and Methods A web-based survey was designed to explore spine surgeons' perceived advantages and disadvantages of the use of intraoperative CT and navigation systems, the learning curve of the technology, its teaching utility, and surgeon comfort level with the technology. Questions were based on the unified theory of acceptance and use of technology (UTAUT) model, which explores four main constructs that directly determine user acceptance and usage behavior: performance expectancy, effort expectancy, social influence, and facilitating conditions. The survey was distributed to surgeon members of the Canadian, New Zealand, and Australian Spine Societies. Participants were stratified into 2 groups: users and non-users of intra-operative CT and navigation, with a slight variability in questions answered by both groups based on applicability. Results A total of 53 surgeons completed the survey (34 Canadians, 16 New Zealanders, and 4 Australians). Overall, 24 of them are users of an intraoperative CT and navigation systems, and 29 are nonusers. The top three advantages identified by both users and nonusers in order of importance were more accurate screw placement, safer screw placement, and reassurance and confirmation of optimal screw placement. The top two disadvantages identified by both users and nonusers were the expense to purchase and maintain the equipment and the exposure of patient to radiation. The third main concern for nonusers is the time consumption associated with the use of this technology, whereas for surgeons who do use it, a disadvantage was that the use of intraoperative CT and navigation is technician dependent. Another concern revealed by the survey is after intra-operative CT and navigation use is implemented at an institution, surgeons think that the use of this technology may present an increased risk of infection (58.3% of surgeons who use it are concerned about this). Surgeons who have adopted this technology have done so primarily due to its clinical efficacy with screw placement, whereas surgeons who have not adopted intra-operative CT and navigation identify the cost of this technology as a limiting factor. Conclusion Spine surgeons recognize the benefit of using intraoperative CT and navigation systems to improve screw placement accuracy and safety. The cost associated with this new technology is the biggest deterrent to its widespread adoption in spine surgery centers in Canada, New Zealand, and Australia. Cost-benefit analysis evaluating the use of this technology would be useful in helping surgeons and health care centers make informed decisions about whether or not this investment is warranted.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.058
GPT teacher head0.395
Teacher spread0.336 · 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

Citations10
Published2015
Admission routes2
Has abstractyes

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