Surgical Sabermetrics
Bibliographic record
Abstract
OBJECTIVE: To evaluate the current evidence for surgical sabermetrics: digital methods of assessing surgical nontechnical skills and investigate the implications for enhancing surgical performance. BACKGROUND: Surgeons need high-quality, objective, and timely feedback to optimize performance and patient safety. Digital tools to assess nontechnical skills have the potential to reduce human bias and aid scalability. However, we do not fully understand which of the myriad of digital metrics of performance assessment have efficacy for surgeons. METHODS: A systematic review was conducted by searching PubMed, EMBASE, CINAHL, and PSYCINFO databases following PRISMA-ScR guidelines. MeSH terms and keywords included "Assessment," "Surgeons," and "Technology". Eligible studies included a digital assessment of nontechnical skills for surgeons, residents, and/or medical students within an operative context. RESULTS: From 19,229 articles screened, 81 articles met the inclusion criteria. The studies varied in surgical specialties, settings, and outcome measurements. A total of 122 distinct objective, digital metrics were utilized. Studies digitally measured at least 1 category of surgical nontechnical skill using a single (n=54) or multiple objective measures (n=27). The majority of studies utilized simulation (n=48) over live operative settings (n=32). Surgical Sabermetrics has been demonstrated to be beneficial in measuring cognitive load (n=57), situation awareness (n=24), communication (n=3), teamwork (n=13), and leadership (n=2). No studies measured intraoperative decision-making. CONCLUSIONS: The literature detailing the intersection between surgical data science and operative nontechnical skills is diverse and growing rapidly. Surgical Sabermetrics may provide a promising modifiable technique to achieve desirable outcomes for both the surgeon and the patient. This study identifies a diverse array of measurements possible with sensor devices and highlights research gaps, including the need for objective assessment of decision-making. Future studies may advance the integration of physiological sensors to provide a holistic assessment of surgical performance.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.009 |
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".