Reproducibility of radioactive iodine uptake (<scp>RAIU</scp>) measurements
Bibliographic record
Abstract
BACKGROUND: Measurement of radioactive iodine uptake (RAIU) is an important aspect of the assessment and treatment of patients with hyperthyroidism. Its uncertainty affects how much of a true change in RAIU can be detected as well as appropriateness of the therapy dosage upon which it is based. In this study, a method of estimating the reproducibility and least significant change (LSC) values for RAIU measurements, and the implications of the values observed are discussed, with emphasis on application to quality assurance initiatives. METHODS: I-NaI in capsule form, RAIU measurements were obtained in duplicate using a thyroid probe uptake system. Assessment of reproducibility was performed using root-mean-square standard deviation. RESULTS: Average difference between duplicated RAIU measurements in our study cohort was -0.1 ± 1.6% and ranged from -4.8% to 3.1%. Reproducibility of probe-based RAIU measurement was calculated to be 1.1% and 95% LSC was 3.2%. CONCLUSION: In our clinic, probe-based RAIU is a reproducible and relatively precise measurement. Using the method we have outlined, each institution can perform reproducibility assessment and compute the LSC of RAIU measurements based on its own staff, iodine isotope, equipment, protocols, and patient population. These values are useful in the assessment of serial change in RAIU, and as more experience is accumulated, can serve as benchmarks to be used in quality assurance initiatives.
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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 | 0.039 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".