Preoperative localization in primary hyperparathyroidism.
Bibliographic record
Abstract
OBJECTIVE: To determine the most effective preoperative localization techniques for patients with primary hyperparathyroidism to facilitate the surgical procedure, decrease patient morbidity, and decrease the number of repeat surgeries owing to inability to locate the abnormal parathyroid gland. METHODS: This was a retrospective study in which 53 patients with primary hyperparathyroidism underwent preoperative sestamibi scanning and ultrasonography. If the two tests failed to agree on the precise location of the abnormal gland, a third imaging technique, magnetic resonance imaging (MRI), was used to confirm the precise location of the gland. Patients with secondary, tertiary, and recurrent hyperparathyroidism and patients with thyroid carcinoma were excluded from this study. Twenty males and 33 females were involved in the study. The mean age was 59.8 years (range 34-84 years). The preoperative results were compared with findings in surgery. A successful surgery was defined as parathyroid hormone and corrected calcium values in the normal range following the operative procedure. RESULTS: There was concurrence between ultrasound and sestamibi scanning in 70% (37 of 53) of the patients. When both agreed, the identified location of the abnormal parathyroid gland was correct 97% (36 of 37) of the time. The ultrasound and sestamibi scanning did not coincide in 30% of the patients (16 of 53). In this scenario, MRI was performed. When the MRI agreed with either of the two previous imaging techniques, the abnormal gland was accurately localized 100% of the time. In six cases (11%), there was no definitive agreement between all three tests that were performed. CONCLUSION: The combination of preoperative ultrasonography and sestamibi scanning is effective in predicting the location of parathyroid adenomas in patients with primary hyperparathyroidism. When both tests conflict, MRI is an effective tool to localize the abnormal glands. This study describes an algorithm for the preoperative localization of parathyroid gland abnormalities, in particular parathyroid adenomas. Second, it allows patients to undergo unilateral neck exploration, as opposed to bilateral neck exploration, where operative times, duration of hospitalization, and patient morbidity are potentially decreased.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".