Rapid standardized operating rooms (RAPSTOR) in thyroid and parathyroid surgery
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of a high efficiency rapid standardized OR (RAPSTOR) for hemithyroid/parathyroid surgery using standardized equipment sets (SES) and consecutive case scheduling (CCS) on turnover times (TOT), average case volumes, patient outcomes, hospital costs and OR efficiency/stress. METHODS: Patients requiring hemithyroidectomy (primary or completion) or unilateral parathyroidectomy in a single surgeon's practice were scheduled consecutively with SES. Retrospective control groups were classified as sequential (CS) or non-sequential (CNS). A survey regarding OR efficiency/stress was administered. Phenomenography and descriptive statistics were conducted for time points, cost and patient outcome variables. Hospital cost minimization analysis was performed. RESULTS: The mean TOT of RAPSTOR procedures (16 min; n = 27) was not significantly different than CS (14 min, n = 14) or CNS (17 min, n = 6). Mean case number per hour was significantly increased in RAPSTOR (1.2) compared to both CS (0.9; p < 0.05) and CNS (0.7; p < 0.05). Average operative time was significantly reduced in RAPSTOR (32 min; n = 28) compared to CNS (48 min; p < 0.05) but not CS (33 min; p = 0.06). Time to discharge was reduced in RAPSTOR (595 min) compared to CNS (1210 min, p < 0.05). There was no difference in complication rate between all groups (p = 0.27). Survey responses suggested improved efficiency, teamwork and workflow. Furthermore, there is associated decrease in direct operative costs for RAPSTOR vs. CS. CONCLUSION: A high efficiency standardized OR for hemithyroid and parathyroid surgery using SES and CCS is associated with improved efficiency and, in this study, led to increased capacity at reduced cost without compromising patient safety. LEVEL OF EVIDENCE: Level 2.
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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 | 0.003 | 0.009 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".