Minimally invasive parathyroidectomy under local anesthesia: patient satisfaction and overall outcome.
Bibliographic record
Abstract
OBJECTIVE: To compare minimally invasive parathyroidectomy (MIP) under local anesthesia (MIPULA) to minimally invasive parathyroidectomy performed under general anesthesia (MIPUGA) in terms of postoperative pain, postanesthetic side effects, patient satisfaction, and overall outcome. DESIGN: Prospective comparative cohort study. METHODS: Consecutive consenting patients presenting to a single surgeon's practice were enrolled into MIPULA or MIPUGA groups if inclusion criteria were satisfied. A standard anesthesia and surgical protocol was followed for all included patients. Subjective outcome measurements (pain, overall satisfaction, and other variables) were achieved through questionnaires. Objective outcomes were also measured. RESULTS: Seventy-four patients were enrolled: 58 in the MIPULA group and 16 in the MIPUGA group. Operative time and hospital stay were significantly shorter in the MIPULA group. Subjectively, the MIPULA group was significantly more ready for discharge versus the MIPUGA group. No significant difference in overall satisfaction between groups was noted. Biochemical cure and conversion (MIPULA to general anesthesia open exploration) rates for our cohort were 100% and 4%, respectively. CONCLUSIONS: MIPULA confers significantly shorter operative time and hospital stay with no significant difference in subjective postoperative pain, patient satisfaction, overall outcome, or cure rate when compared to MIPUGA. Provided that appropriate preoperative localization and surgical experience are present, MIPULA can be offered to patients as a safe and reasonable alternative to MIPUGA.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".