Case-Controlled Comparison of Video-Assisted and Conventional Minimally Invasive Parathyroidectomy
Bibliographic record
Abstract
Video-assisted parathyroidectomy (VAP) is a new approach to parathyroid exploration for primary hyperparathyroidism (PH). We examined the VAP learning curve and hypothesized that compared with conventional minimally invasive parathyroidectomy (MIS), VAP has similar complication rates and the added benefit of a shorter hospital length of stay. Using a case-control study design, patients with PH with single-focus imaging results undergoing VAP or MIS were compared during a 5-year VAP implementation period. VAP was possible in 18 per cent of patients undergoing initial parathyroid exploration. In comparing 125 VAP cases with 95 MIS control subjects, patients undergoing MIS had higher mean preoperative levels of calcium (P = 0.007) and parathyroid hormone (P = 0.008), greater mean adenoma weight (P < 0.001), and increased long-term mortality (4% MIS vs 0% VAP, P = 0.03). Mean operative time, in-house analgesia use, and operative complications did not differ. The rate of conversion from VAP to MIS was 14 per cent. Patients undergoing VAP were less likely to require an overnight hospital stay (P = 0.01). VAP is a safe surgical option for selected patients with PH, offering improved cosmesis with operative times comparable to conventional MIS. VAP can be done with a low conversion rate even during implementation and allows the added benefit of shorter hospital stay.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".