The use of computer-assisted surgery as an educational tool for the training of orthopedic surgery residents in pedicle screw placement: a pilot study and survey among orthopedic residents
Bibliographic record
Abstract
<h3>Background:</h3> The training of orthopedic residents in adequate pedicle screw placement is very important. We sought to investigate orthopedic residents’ perspectives on the use of computer-assisted surgery (CAS) in a training trial. <h3>Methods:</h3> Orthopedic residents were randomly assigned to independently place a screw using the free-hand technique and the CAS technique on 1 of 3 cadavers (Cobb angles 5º, 15º and 67º) at randomly selected thoracolumbar vertebral levels. All residents were blinded to their colleagues’ pedicle screw placements and were asked to complete a short questionnaire at the end of the session to evaluate their experience with CAS. We obtained CT images for each cadaver to assess pedicle screw placement accuracy and classified placement as A) screw completely in pedicle, B) screw < 2 mm outside pedicle, C) screw 2–4 mm outside pedicle, or D) screw > 4 mm outside pedicle. <h3>Results:</h3> Twenty-four orthopedic residents participated in this trial study. In total, 65% preferred using the free-hand technique in an educational setting even though most (60%) said that CAS is safer. The main reason for free-hand technique preference was the difficult technical aspects encountered with CAS. In addition, accuracy of pedicle screw placement in this trial showed that 5 screws were classified as A or B (safe zone) and 19 as grade C or D (unsafe zone) using the free-hand technique compared with 15 and 9, respectively, using CAS (<i>p</i> = 0.008). <h3>Conclusion:</h3> Orthopedic residents perceived CAS as safe and demonstrated improved accuracy in pedicle screw placement in a single setting. However, the residents preferred the free-hand technique in an educational stetting owing to the difficult technical aspects of CAS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".