Dietary Iodine Restriction in Preparation for Radioactive Iodine Treatment or Scanning in Well-Differentiated Thyroid Cancer: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Dietary iodine is often restricted before radioactive iodine (RAI) scanning or treatment of well-differentiated thyroid cancer. Our objective was to examine the impact of a low-iodine diet (LID) before RAI treatment or scanning on the following outcomes: (i) the efficacy of thyroid remnant ablation (or residual disease elimination), (ii) urinary iodine measurements, (iii) RAI kinetics, and (iv) long-term thyroid cancer outcomes. METHODS: We performed a systematic review of the English literature. We searched four electronic databases and conducted a hand search. Two reviewers independently screened citations and reviewed full-text articles and reached consensus on included articles. Two reviewers independently abstracted data. RESULTS: We reviewed 76 abstracts or citations and 26 full-text articles. Eight studies were included in the review. The most commonly studied diets allowed ≤ 50 µg/day of iodine for 1-2 weeks. In one study, 6-month successful remnant ablation rates were higher in patients following an LID than in controls. However, in another study, there was no significant benefit of an LID. LIDs reduce urinary iodine measurements and appear to increase I-131 uptake or lesional radiation compared to regular diets. No studies have examined long-term recurrence or mortality rates. CONCLUSIONS: Given that LIDs reduce urinary iodine measurements, increase I-131 uptake, and possibly improve efficacy of I-131 treatment, we currently favor the use of a 1-2-week LID before I-131 therapy or scanning. However, more research is needed to clarify the role of this dietary intervention.
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 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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".