Radio-guided minimally invasive parathyroidectomy under local anesthesia
Bibliographic record
Abstract
BACKGROUND: Despite the high success rate of the complete bilateral neck exploration to treat primary hyperparathyroidism, less invasive alternatives have been emerging. In an attempt to reduce operative time and decrease perioperative morbidity, we reported our experience with radio-guided minimally invasive parathyroidectomy (RMIP) under local anesthesia. STUDY DESIGN: A retrospective chart review was carried out to study 55 consecutive patients, in an adult tertiary care hospital (Montreal General Hospital), who underwent RMIP under local anesthesia over a 30-month period. Charts were reviewed for operative information, radiological and pathological diagnoses and post-operative course. The main outcome measures were the accuracy of localizing the parathyroid adenoma, operative time, achievement of normocalcemia post-operatively and perioperative morbidity. RESULTS: Of the 55 patients we studied, 51 were cured as defined by normocalcemia following a single intervention, for an overall cure rate of approximately 93%. Four patients required an additional procedure: In two because of failure to remove a diseased gland, and in two because of multiglandular disease. The preoperative sestamibi scan accurately predicted the location of all abnormal parathyroid glands in 53 cases. In the remaining two cases, the scan failed to predict multiglandular disease. Average total operative time was 39 minutes. There were no major complications. CONCLUSIONS: RMIP under local anesthesia is a safe and effective modality to treat primary hyperparathyroidism. The short operative time, the use of local anesthesia and the low complication rate make this technique a viable alternative.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".