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A surgical algorithm for the management of retrosternal goiters

2022· preprint· en· W4301861023 on OpenAlexaff
Tsu‐Hui Low, George A. Petrides, Richard Inculet, Kevin Fung, Anthony C. Nichols, S. Danielle MacNeil, John Yoo

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicThyroid and Parathyroid Surgery
Canadian institutionsWestern UniversityLondon Health Sciences Centre
Fundersnot available
KeywordsMedicineThyroidectomyThoracotomyGoiterPericardiumSurgeryAlgorithmGeneral surgeryThyroidComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

Key points: - The heterogeneity of retrosternal goiter size and location, nearby anatomical structures, and evolving surgical techniques means selecting the most appropriate individualised surgical approach remains challenging. - Although most retrosternal goiters can be removed transcervically, a small proportion of patients fulfill anatomical and radiological criteria for concurrent extracervical approaches based on decades of experience at the London Health Sciences Centre. - Anterior goiters above the level of the pericardium were resected using cervical thyroidectomy with either mediastinoscopic-assisted delivery or cervical thyroidectomy with video- assisted thyroidectomy (VATS) - Anterior goiters extending beyond the pericardium require median sternotomy to facilitate sufficient vascular control and exposure. - Posteriorly extending goiters can be managed using trans-thoracic approaches including lateral thoracotomy or VATS.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.639
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.313
Teacher spread0.280 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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