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Record W2885738823 · doi:10.1097/prs.0000000000004944

An Intraoperative Three-Dimensional Imaging System for Better Image Sharing and Protection of Reconstructive Surgeons’ Neck

2018· letter· en· W2885738823 on OpenAlexaboutno aff
Yuma Fuse, Takumi Yamamoto

Bibliographic record

VenuePlastic & Reconstructive Surgery · 2018
Typeletter
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsnot available
Fundersnot available
KeywordsMagnificationMicrosurgeryMedicineOperating microscopeAnastomosisSurgical instrumentSurgeryMicroscopeMedical physicsComputer scienceComputer visionPathology

Abstract

fetched live from OpenAlex

Sir: We read with great interest the article entitled “Work-Related Musculoskeletal Injuries in Plastic Surgeons in the United States, Canada, and Norway” by Khansa et al.1 Their work is of clinical significance because they elucidated that plastic surgeons often suffer from musculoskeletal symptoms caused by statistic postures during microsurgery. Since microsurgery was introduced, various operations using the microscope have been performed in plastic surgical fields.2 In ordinary situations, a microscope has been used only for microvessel anastomosis or microdissection. Mendez et al. reported the efficacy of use of the heads-up three-dimensional microscope for anastomosis of small vessels in rats, in comfortable postures.3 In this study, we applied a novel heads-up three-dimensional microscope, with a wide variety of magnifications, to flap elevation, with a favorable advantage. A three-dimensional microscope (KestrelView II; Mitaka Kohki Co., Tokyo, Japan) was set over the surgical field at 300- to 1000-mm working distance. During flap elevation, the surgeon performed the operation while watching the monitor. In accordance with the surgical process, the angle of the camera was adjusted and the magnification was 2× to 5× mainly. The assistant surgeons also watched the same video on the screen set at the opposite side, which enabled them to share the principal surgeon’s view and discuss the surgical procedure (Fig. 1). All of the surgeons were able to see the original surgical field by just looking down. The whole operative video was recorded.Fig. 1.: Surgeons are shown sharing the same surgical view and discussing the operation, using a heads-up three-dimensional microscope in a comfortable posture.Musculoskeletal injuries are common among plastic surgeons.1,4 By using this intraoperative three-dimensional imaging system, surgeons can share the operator’s original view, which enables them to discuss the operation while watching the same video simultaneously. The camera is set at adequate distance for working, so surgeons can perform the surgical procedure without any trouble, in a comfortable posture. In addition, because of its wide range of magnification, the entire operation can be performed under this microscope—from skin incision to vessel anastomosis. Under higher magnification, we can observe tiny anatomy and perform meticulous dissection. Also, operators can compare the microscopic view and the surgical field by just looking down. In ordinary situations, the assistant operator stands at the opposite side of the main surgeon, so they cannot share the main surgeon’s original view: they watch the 180-degree opposite view. However, using this heads-up three-dimensional microscope, all of the surgeons can get the same view. Furthermore, surgical video can be recorded, so this is very useful for review and education. Because this is the first report of the microscope’s application in reconstructive microsurgery, further investigations are required to confirm the efficacy of the microscope. DISCLOSURE The authors have no financial interest to declare in relation to the content of this communication. There were no sources of support for this work. Yukari Ando, M.D.Yuma Fuse, M.D.Takumi Yamamoto, M.D., Ph.D.Department of Plastic and Reconstructive SurgeryNational Center for Global Health and MedicineTokyo, Japan

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.029
GPT teacher head0.259
Teacher spread0.230 · 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.

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

Citations2
Published2018
Admission routes1
Has abstractyes

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