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Record W6925187660 · doi:10.17605/osf.io/4y7kd

Thyroid Auto-Transplantation: A Scoping Review

2023· other· en· W6925187660 on OpenAlexaff

Bibliographic record

VenueOpen Science Framework · 2023
Typeother
Languageen
FieldMedicine
TopicThyroid and Parathyroid Surgery
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLevothyroxineThyroidThyroidectomySubclinical infectionSystematic reviewThyroid cancerGold standard (test)

Abstract

fetched live from OpenAlex

Total thyroidectomy is currently considered the gold standard for management of bilateral benign thyroid disorders. However, it requires life-long levothyroxine replacement therapy (LRT) which demands a high level of patient compliance, regular follow-up visits as well as long-term risks including frequent subclinical hypo- and hyperthyroidism episodes, long-term effects on bone mineral density in post-menopausal woman, and its drug absorption is hindered in patients with GI and other disorders. Thyroid auto transplantation (TATx) is a potential alternative to LRT and entails the transplantation of healthy thyroid tissue in another place in the body so that the patient can still produce and regulate thyroid hormones, making LRT not necessary. As a new and emerging topic of interest, a scoping review on TATx for the management of patients undergoing thyroidectomy will be performed to provide a preliminary assessment of the existing research available and a more cohesive understanding of the benefits and limitations of TATx as an alternative to LRT. Animal, translational and human studies will be included and compared to identify any differences in methodology, results, sample size and patient demographics, and summarized to offer new insights on the feasibility, safety and efficacy of TATx. As few systematic or scoping reviews exist on this topic, this research is needed to provide an up-to-date assessment of the existing body of knowledge and provide direction for future research and evaluation of TATx.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.645
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.004

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 head0.047
GPT teacher head0.397
Teacher spread0.350 · 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; both teacher heads agree on what is shown here.

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
Published2023
Admission routes1
Has abstractyes

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