Effect of stunning of diagnostic 131-iodine doses on ablative doses for differentiated thyroid cancer patient’s outcome
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".