MINIMALLY INVASIVE PARATHYROIDECTOMY: AN AUDIT OF A CHANGE IN CLINICAL PRACTICE
Bibliographic record
Abstract
BACKGROUND: Minimally invasive parathyroidectomy (MIP) for primary hyperparathyroidism is gaining acceptance as a useful tool in the armamentarium of the endocrine surgeon. METHODS: We undertook an audit of 154 consecutive cases of parathyroidectomy carried out through bilateral neck exploration as well as a minimally invasive approach. RESULTS: Bilateral neck exploration had a 100% single operation cure rate. MIP had a 90% cure rate. Sestamibi localization had a positive predictive value of 99% for identifying an abnormal parathyroid gland. However, it performed poorly in the presence of multiglandular disease, resulting in these patients being at risk of having persistent hyperparathyroidism and therefore requiring a second operation. CONCLUSION: Our results with bilateral neck exploration are favourable compared with other large series. However, we have reported a 10% reoperation rate with MIP. Although not ideal, we are confident that, as a result of improvements based on this audit and with increasing experience, the cure rate will improve to reach international benchmarks. As such we feel that this strategy is a pragmatic way to offer MIP to patients in our region.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".