Introduction of a Structured Assessment of Clinical Competency for Fellows in Gynecologic Oncology: A Pilot Study
Bibliographic record
Abstract
Background: Gynecologic oncology surgery is recognized as a highly complex surgical specialty. Technical skill is the most critical expertise that a gynecology oncologist must acquire. Objective assessment of these skills is both valuable and necessary. Objectives: This observational cohort study study had two objectives: (1) to demonstrate the feasibility of running national assessments for technical and communication skills for gynecologic oncology fellows (GOFs) in an annual conference setting; and (2) to demonstrate the design of a technical-skills assessment examination relevant to GOFs. Materials and Methods: All fellows in attendance at the conference were invited to participate in the Objective Assessment of Technical Skills (OSATS) study. Expert examiners evaluated each skills station. Results: Eight, of a possible 14, volunteer fellows participated in the pilot test. There was a statistical difference between candidates for laparoscopic vault closure (p=0.029). The global rating scale was able to determine a statistically significant difference skill level between Year 1 and Year 2 fellows (p=0.016). Laparoscopic suturing and breaking bad news were the competencies identified as requiring most improvement for fellows. Conclusions: It is feasible to assess GOFs' technical and communication skills objectively as part of a national continuing education meeting. Devoting further resources to objective skills evaluation is justified for GOFs. (J GYNECOL SURG 31:17)
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".