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Record W1991510117 · doi:10.5430/jst.v3n6p11

Effect of stunning of diagnostic 131-iodine doses on ablative doses for differentiated thyroid cancer patient’s outcome

2013· article· en· W1991510117 on OpenAlexvenueno aff
Khaled Elsaban, Hiji Al-Sakhri, Abdullah Al-Zahrani

Bibliographic record

VenueJournal of Solid Tumors · 2013
Typearticle
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAblative caseThyroid cancerNuclear medicineThyroidIodineRadiologyInternal medicineRadiation therapy

Abstract

fetched live from OpenAlex

Background: Thyroid stunning was defined as transient reduction of thyroid tissue uptake 131I (RAI-131) ablative dose after a diagnostic 131I dose that decreases the final absorbed dose in ablative therapy. Aim of the study: after following the proper precautions compare the response to the ablative dose given to patients with differentiated thyroid cancer with or without diagnostic radioactive iodine 131(RAI-131). Patients and methods: One hundred patients with differentiated thyroid cancer were included and divided into two groups: Group I, ablative dose of RAI-131according to their risk stratification without diagnostic dose and Group II, patients performing diagnostic whole body scan [5mCi] followed by ablative dose. Results: The current study have showed no significant associations between overall response in both groups and the different studied parameters except for the mean ablative dose of RAI-131 [r=0.9; P<0.001 and r=0.7, P<0.001 in group I and group II respectively]. Correlation matrix was used in all patients revealed that overall response was highly correlated with risk stratification; cervical nodal status and RAI-131 ablative dose [ P values <0.01; <0.01 and <0.01 respectively], while regression proved that the only predictor for response is the mean RAI-131 ablative dose. Conclusion: Following the proper precautions prevent stunning appearance after the diagnostic dose success rate to ablation will not be affected.

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)0.001
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 score0.341
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.018
GPT teacher head0.329
Teacher spread0.311 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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