Describing the spine surgery learning curve during the first two years of independent practice
Bibliographic record
Abstract
ABSTRACT: Retrospective cohort studyTo characterize the learning curve of a spine surgeon during the first 2 years of independent practice by comparing to an experienced colleague. To stratify learning curves based on procedure to evaluate the effect of experience on surgical complexity.The learning curve for spine surgery is difficult to quantify, but is useful information for hospital administrators/surgical programs/new graduates, so appropriate expectations and accommodations are considered.Data from a retrospective cohort (2014-2016) were analyzed at a quaternary academic institution servicing a geographically-isolated, mostly rural area. Procedures included anterior cervical discectomy and fusion, posterior cervical decompression and stabilization, single and 2-level posterior lumbar interbody fusion, lumbar discectomy, and laminectomy. Data related to patient demographics, after-hours surgery, and revision surgery were collected. Operative time was the primary outcome measure, with secondary measures including cerebrospinal fluid leak and early re-operation. Time periods were stratified into 6 month quarters (quarter [Q] 1-Q4), with STATA software used for statistical analysis.There were 626 patients meeting inclusion criteria. The senior surgeon had similar operative times throughout the study. The new surgeon demonstrated a decrease in operative time from Q1 to Q4 (158 minutes-119 minutes, P < .05); however, the mean operative time was shorter for the senior surgeon at 2 years (91 minutes, P < .05). The senior surgeon performed more revision surgeries (odds ratio [OR] 2.5 [95% confidence interval [CI] 1.7-3.6]; P < .001). Posterior interbody fusion times remained longer for the new surgeon, while laminectomy surgery was similar to the senior surgeon by 2 years. There were no differences in rates of cerebrospinal fluid leak (OR 1.2 [95% CI 0.6-2.5]; P > .05), nor reoperation (OR 1.16 [95% CI 0.7-1.9]; P > .05) between surgeons.A significant learning curve exists starting spine practice and likely extends beyond the first 2 years for elective operations.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".