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Record W3031377430 · doi:10.4174/astr.2020.98.6.307

Comparative study of bilateral axillo-breast approach endoscopic and robotic thyroidectomy: propensity score matching analysis of large multi-institutional data

2020· article· en· W3031377430 on OpenAlexaff
June Young Choi, In Eui Bae, Hyun Soo Kim, Sang Gab Yoon, Jin Wook Yi, Hyeong Won Yu, Su‐jin Kim, Young Jun Chai, Kyu Eun Lee, Yeo‐Kyu Youn

Bibliographic record

VenueAnnals of Surgical Treatment and Research · 2020
Typearticle
Languageen
FieldMedicine
TopicThyroid and Parathyroid Surgery
Canadian institutionsSt. Peter's Hospital
Fundersnot available
KeywordsMedicinePropensity score matchingHypoparathyroidismSurgeryThyroidectomyDissection (medical)ThyroidInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: The aim of this study was to compare the large multi-institutional data of surgical outcomes of bilateral axillo-breast approach (BABA) robotic (RT) and endoscopic thyroidectomy (ET) and to evaluate the merits of robotic thyroidectomy. METHODS: From 2004 to 2015, 1,029 patients underwent BABA ET, and from 2008 to 2015, 2003 patients underwent BABA RT in 3 large volume centers in Korea. Two groups were retrospectively compared in terms of clinicopathologic characteristics, complications, surgical completeness, and long-term outcomes using propensity score matching analysis. RESULTS: RT 92.7%, P < 0.001). In long-term follow-up of cancer patients, 1.4% experienced recurrence after ET (10 cases), while 0.3% cases experienced recurrence after RT (5 cases) (P < 0.001). CONCLUSION: Both ET and RT can be safe and effective methods to treat thyroid diseases. However, the application of robotic system may help to overcome the limitations of the instruments and surgeon's skills.

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 score0.011
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.521
GPT teacher head0.464
Teacher spread0.058 · 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

Citations24
Published2020
Admission routes1
Has abstractyes

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