Total Thyroidectomy: the first, the best. The recurrent goiter issue.
Bibliographic record
Abstract
BACKGROUND: Redo surgery for recurrent goiter is still now, even in experienced hands, followed by higher morbidity than primary total thyroidectomy. Suppressive Levothyroxine therapy failed to improve the recurrence rate, while inducing a subclinical hyperthyroidism. Aim of this study is to verify morbidity after total thyroidectomy for benign thyroid diseases, both primary and after recurrence. MATERIALS AND METHODS: A series of 20 cases of total thyroidectomy for recurrent benign diseases (RG), performed between January 2001 and December 2013 was compared with 225 cases of primary total thyroidectomy (PT) . Cancers, even incidentally diagnosed, were excluded. At least a 12 months follow up was accomplished. Due to the small size of the sample for RG, statistical analysis was performed by Fisher test only. RESULTS: Postoperative complications were Transient hypocalcemia: 5 (25%) in RG and 18 (8%) in PT, Permanent hypocalcemia only 2 (10%) in RG (significant for p <0,05), Transient RLN deficit 5 (25 %) in RG and 6 (2.6%) in PT (significant for p< 0.05). CONCLUSIONS: Differences in incidence of perioperative complications cannot be advocated to justify a less than total thyroidectomy even in benign disease setting. The need for a redo surgery with its burden of morbidity is per se a good reason to avoid a conservative surgery. Further, suppressive therapy with Levothyroxine often fails to avoid recurrence, inducing in some cases a specific morbidity. Our experience confirms the results of our previous experiences and of literature on this topic: the best management of recurrent goiter is its prevention by primary total thyroidectomy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".