Evaluating the learning curve of Minimally Invasive Chevron and Akin Osteotomy for correction of hallux valgus deformity: a systematic review
Bibliographic record
Abstract
Abstract Background One procedure that has gained popularity in the surgical management of hallux valgus is the minimally invasive Chevron and Akin osteotomy (MICA). The purpose of this systematic review was to evaluate the learning curve associated with this technically demanding procedure. Methods A search of the EMBASE and PubMed databases was performed to identify all clinical studies that assessed the learning curve associated with the MICA procedure. Studies where patients were not diagnosed with hallux valgus, did not undergo MICA, or did not report data on operation time, fluoroscopy exposure, or complications were excluded. A risk of bias assessment was conducted to assess the validity of the studies. Results The initial literature search yielded 287 studies, and seven studies were included in the final analysis. A quantitative comparative analysis could not be performed as the included studies used different statistical methods to quantify the learning curve. Lewis et al. determined that after 38 operations, there was a decrease in operation time and fluoroscopy exposure (p < .001). Merc et al. found that it took 29 and 30 operations to reach a plateau for operation time and fluoroscopy exposure, respectively (p < .001). Palmanovich et al. found that it took 20 and 26 operations to reach a plateau for operation time and fluoroscopy exposure, respectively (p < .001). Toepfer and Strässle found there was a significant decrease in operation time and fluoroscopy exposure after the first 19 procedures in their series (p < .001). With respect to complications, one study found a significant difference after the 42nd operation (p = .007). However, the remaining studies found that complication rates did not significantly change with increased technical proficiency. All seven studies were deemed to have a moderate risk of bias. Conclusions Surgeons can expect a learning curve of 20 to 40 operations before reaching technical proficiency with the MICA procedure. After the learning curve is achieved, surgeons can expect to see a significant decrease in both operation times and fluoroscopy exposure. No consistent significant difference was found in complications as one becomes more technically proficient with the procedure.
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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 | 0.015 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".