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Data Sheet 1_Multidisciplinary Canadian consensus on the multimodal management of high-risk and radioactive iodine-refractory thyroid carcinoma.docx

2024· dataset· en· W6908552496 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2024
Typedataset
Languageen
FieldArts and Humanities
TopicHistorical and Cultural Studies of Poland
Canadian institutionsnot available
Fundersnot available
KeywordsThyroidReimbursementThyroid carcinomaMultidisciplinary approachRadioactive iodineRadiation therapyRadiation oncologistModality (human–computer interaction)Radiation oncology

Abstract

fetched live from OpenAlex

Most follicular cell-derived differentiated thyroid carcinomas are regarded as low-risk neoplasms prompting conservative therapeutic management. Here, we provide consensus recommendations reached by a multidisciplinary group of endocrinologists, medical oncologists, pathologists, radiation oncology specialists, a surgeon and a medication reimbursement specialist, addressing more challenging forms of this malignancy, focused on radioactive iodine (RAI)-resistant or -refractory differentiated thyroid carcinoma (RAIRTC). In this document we highlight clinical, radiographic, and molecular features providing the basis for these management plans. We distinguish differentiated thyroid cancers associated with more aggressive behavior from thyroid cancers manifesting as poorly differentiated and/or anaplastic carcinomas. Treatment algorithms based on risk-benefit assessments of different multimodal therapy approaches are also discussed. Given the scarcity of data supporting management of this rare yet aggressive disease entity, these consensus recommendations provide much needed guidance for multidisciplinary teams to optimally manage RAIRTC.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.201
Threshold uncertainty score0.998

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0600.003

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 head0.060
GPT teacher head0.249
Teacher spread0.189 · 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
GenreDataset

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
Published2024
Admission routes1
Has abstractyes

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