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Record W2628286455 · doi:10.1089/ve.2017.0096

Personalized Selection Criteria for Radioiodine Administration in Low- and Intermediate-Risk Papillary Thyroid Carcinoma

2017· article· en· W2628286455 on OpenAlexaffabout
Paul G. Walfish

Bibliographic record

VenueVideoEndocrinology · 2017
Typearticle
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsMount Sinai HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineThyroid carcinomaThyroglobulinThyroidectomyThyroidRadioactive iodineProtocol (science)Internal medicinePathologyAlternative medicine

Abstract

fetched live from OpenAlex

Our center has developed a personalized strategy based on objective selection criteria for radioactive iodine (RAI) administration after a total thyroidectomy in ≥1 cm low- and intermediate-risk papillary thyroid carcinoma (PTC) patients. These recommendations have been derived from a prospective long-term protocol followed for >8 years on such PTC patients not receiving routine RAI using postsurgical pathology analysis findings in combination with serial stimulated serum thyroglobulin and neck ultrasonography measurements. Our observations have indicated that the vast majority of such PTC patients (116 of 129 = 90%) could safely avoid RAI therapy and that previous evidenced-based clinical risk factors considered as indications for RAI did not correlate with our proposed individualized RAI selection protocol criteria. The routine application of this RAI selection strategy also greatly reduced patient anxiety/inconvenience, potential radiation side effects, and healthcare costs. Paul G. Walfish has received consulting fees from Sanofi Genzyme Canada and is a shareholder in Proteocyte Diagnostics Incorporated. Runtime of video: 15 mins 27 secs This lecture was presented at the 86th Annual Meeting of the American Thyroid Association, Denver, Colorado, September 24, 2016.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.632

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)0.0000.000

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 head0.023
GPT teacher head0.323
Teacher spread0.300 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

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