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Record W4386165098 · doi:10.3389/fendo.2023.1233956

TOETVA parathyroid autofluorescence detection: hANDY-i endoscopy attachment

2023· article· en· W4386165098 on OpenAlexaff
Stefanie Seo, Khalid Mohamed Ali, Samantha A. Wolfe, Nimesh Nagururu, Andy S. Ding, Dipan Desai, R. Alex Harbison, Yoseph Kim, Bo Ning, Richard Jaepyeong, Jonathon O. Russell

Bibliographic record

VenueFrontiers in Endocrinology · 2023
Typearticle
Languageen
FieldMedicine
TopicThyroid and Parathyroid Surgery
Canadian institutionsUniversity Health Network
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institutes of Health
KeywordsAutofluorescenceMedicineEndoscopeEndoscopyThyroidectomyHypoparathyroidismSurgeryThyroidRadiologyInternal medicineFluorescence

Abstract

fetched live from OpenAlex

Background: Treatment options for thyroid pathologies have expanded to include scarless and remote access methods such as the transoral endoscopic thyroidectomy vestibular approach (TOETVA). Currently, no standardized methods exist for locating parathyroid glands (PGs) in patients undergoing TOETVA, which can lead to parathyroid injury and subsequent hypocalcemia. This early feasibility study describes and evaluates the hANDY-i endoscopic attachment for detecting PGs in transoral thyroidectomy. Methods: We used a prototype parathyroid autofluorescence imager (hANDY-i) that was mounted to a 10-mm 0-degree endoscope. The device delivers a split screen view of Red-green-blue (RGB) and near-infrared autofluorescence (NIRAF) which allows for simultaneous anatomical localization and fluorescence visualization of PGs during endoscopic thyroid dissection. Results: One cadaveric case and two patient cases were included in this study. The endoscopic hANDY-i imaging system successfully visualized PGs during all procedures. Conclusion: The ability to leverage parathyroid autofluorescence during TOETVA may lead to improved PG localization and preservation. Further human studies are needed to assess its effect on postoperative hypocalcemia and hypoparathyroidism.

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)0.000
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.019
GPT teacher head0.286
Teacher spread0.267 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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