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Record W4252656401 · doi:10.1002/9780471420194.tnmm48

Thyroid

2017· other· en· W4252656401 on OpenAlexaff
James D. Brierley, David P. Goldstein, Monika K. Krzyzanowska

Bibliographic record

VenueTNM Online · 2017
Typeother
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineThyroidThyroid cancerFollicular thyroid cancerAnaplastic thyroid cancerFollicular cellThyroid lymphomaRadiation therapyMedullary thyroid cancerOncologyPathologyPapillary thyroid cancerInternal medicineCancer researchThyroiditis

Abstract

fetched live from OpenAlex

Summary Thyroid cancer can develop from follicular cells of the thyroid, including papillary and follicular (differentiated thyroid cancer), insular and anaplastic thyroid cancers, and those derived from C cells of the thyroid, the medullary thyroid cancers. The usual presentation and work‐up will be discussed. The initial treatment for all thyroid malignancies (lymphoma excepted) is surgery. The controversies with regard to the extent of surgery and central node dissection will be reviewed, as will the role of radioactive iodine in low‐ and intermediate‐risk differentiated thyroid cancer. Although limited, there is a role for external beam radiotherapy in locally‐advanced thyroid cancer, especially anaplastic, and indications will be given. The role of chemotherapy is limited, but there is increasing evidence of activity of targeted agents.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.022
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0160.001

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 head0.025
GPT teacher head0.332
Teacher spread0.307 · 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 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
Published2017
Admission routes1
Has abstractyes

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