Learning Curve Analysis of the Collum Femoris Preserving Total Hip Surgical Technique
Bibliographic record
Abstract
The aim of this study was to determine whether femoral neck preserving total hip arthroplasty would become less difficult and more efficient during the first 20 cases and to identify potential pitfalls during the introduction of this procedure. The difficulty and efficiency of the initial 20 procedures performed by four surgeons was prospectively determined by analysing a total of 68 video recordings using time-action analysis. This method measures the duration and efficiency of individual actions needed for a surgeon to achieve his or her goal. Afterwards, we reviewed all actions with a long duration and discussed possible causes of delay with the surgeons to identify possible pitfalls. We found a decrease of difficulty and an increase of efficiency during the first 20 cases and a more consistent execution after the initial five cases. Estimating the correct osteotomy level and stem curvature was often difficult, which resulted in a variable stem position. Radiologic analysis demonstrated a tendency for varus position and increased leg length throughout the series, even after the surgeons demonstrated technical proficiency.
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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 | 0.001 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".