A Smart Tool for Intraoperative Leg Length Targeting in Total Hip Arthroplasty: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Leg length discrepancies following total hip arthroplasty (THA) may necessitate subsequent interventions, from heel lifts to revision surgery. Current intraoperative methods of determining leg length are either inaccurate or expensive and invasive. OBJECTIVE: , Inc., Waterloo, ON) to provide accurate, real-time leg length measurements during THA. METHODS: We retrospectively reviewed the medical records of 25 patients who underwent THA utilizing the Intellijoint HIP smart tool between February and August 2014. Intraoperative leg length data was compared with radiographic leg length calculations. Two observers blinded to the Intellijoint HIP findings independently assessed all post-procedure radiographs. RESULTS: The mean difference between smart tool and radiographic measurements was 1.3 mm [CI: -0.1, 2.7]. 88% (22/25) of intraoperative measurements were within 5 mm of radiographic measurements; 100% (25/25) were within 10 mm. A Bland-Altman analysis showed excellent agreement, with 96% (24/25) of measurements within the statistical limit for acceptable agreement, and 84% (21/25) within the clinically acceptable range (± 5 mm). Removal of the first 13 procedures (surgeon training) decreased the mean difference between methods to 0.6 mm [-0.6, 1.9]. All post-training procedures were associated with a difference of <5 mm. There were no reported adverse events related to the use of the smart tool. CONCLUSION: The Intellijoint HIP smart tool is a safe and accurate tool for providing intraoperative measurements of leg length in real-time.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".