Learning Curve Analysis of Different Stages of Robotic-Assisted Laparoscopic Hysterectomy
Bibliographic record
Abstract
Objective . To analyze the learning curves of the different stages of robotic-assisted laparoscopic hysterectomy. Design . Retrospective analysis. Design Classification . Canadian Task Force classification II-2. Setting . Kaohsiung Medical University Hospital, Kaohsiung, Taiwan. Patient Intervention . Women receiving robotic-assisted total and subtotal laparoscopic hysterectomies for benign conditions from May 1, 2013, to August 31, 2015. Measurements and Main Results . The mean age, body mass index (BMI), and uterine weight were <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mn fontstyle="italic">46.44</mml:mn><mml:mo>±</mml:mo><mml:mn fontstyle="italic">5.31</mml:mn></mml:math> years, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mn fontstyle="italic">23.97</mml:mn><mml:mo>±</mml:mo><mml:mn fontstyle="italic">4.75</mml:mn></mml:math> kg/m 2 , and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mn fontstyle="italic">435.48</mml:mn><mml:mo>±</mml:mo><mml:mn fontstyle="italic">250.62</mml:mn></mml:math> g, respectively. The most rapid learning curve was obtained for the main surgery console stage; eight experiences were required to achieve duration stability, and the time spent in this stage did not violate the control rules. The docking stage required 14 experiences to achieve duration stability, and the suture stage was the most difficult to master, requiring 26 experiences. BMI did not considerably affect the duration of the three stages. The uterine weight and the presence of adhesion did not substantially affect the main surgery console time. Conclusion . Different stages of robotic-assisted laparoscopic hysterectomy have different learning curves. The main surgery console stage has the most rapid learning curve, whereas the suture stage has the slowest learning curve.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".