Thyroid Auto-Transplantation: A Scoping Review
Bibliographic record
Abstract
Total thyroidectomy is currently considered the gold standard for management of bilateral benign thyroid disorders. However, it requires life-long levothyroxine replacement therapy (LRT) which demands a high level of patient compliance, regular follow-up visits as well as long-term risks including frequent subclinical hypo- and hyperthyroidism episodes, long-term effects on bone mineral density in post-menopausal woman, and its drug absorption is hindered in patients with GI and other disorders. Thyroid auto transplantation (TATx) is a potential alternative to LRT and entails the transplantation of healthy thyroid tissue in another place in the body so that the patient can still produce and regulate thyroid hormones, making LRT not necessary. As a new and emerging topic of interest, a scoping review on TATx for the management of patients undergoing thyroidectomy will be performed to provide a preliminary assessment of the existing research available and a more cohesive understanding of the benefits and limitations of TATx as an alternative to LRT. Animal, translational and human studies will be included and compared to identify any differences in methodology, results, sample size and patient demographics, and summarized to offer new insights on the feasibility, safety and efficacy of TATx. As few systematic or scoping reviews exist on this topic, this research is needed to provide an up-to-date assessment of the existing body of knowledge and provide direction for future research and evaluation of TATx.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".