Preoperative Pelvic Tilt Analysis Reduces Fluoroscopy Time in Direct Anterior Total Hip Arthroplasty
Bibliographic record
Abstract
The direct anterior approach for total hip arthroplasty (THA) requires the use of intraoperative imaging, exposing the patient and surgical team to radiation. The authors hypothesized that calculation of the preoperative pelvic tilt angle and communication of this value with the fluoroscopy technician may result in a decrease in intraoperative fluoroscopy use. The study also examined total radiation exposure during the procedure to ensure that it was within safe limits. The pelvic tilt was calculated preoperatively for 100 consecutive patients undergoing THA by the direct anterior approach. The fluoroscopy technician was blinded to the value of pelvic tilt for the first 50 cases (control group), with the angle being communicated for the following 50 cases (test group). The total duration of fluoroscopy use for each case was recorded. The values were compared for the 2 experienced technicians involved in the study. The surgeon was blinded to the duration of fluoroscopy use in all cases. Mean fluoroscopy time was 28.65 seconds in the control group and 23.61 seconds in the test group (P=.033). No significant difference in duration of fluoroscopy use was found between the 2 fluoroscopy technicians. The control group and the test group were within safe limits of radiation exposure to both the patient and the surgical team. Preoperative pelvic tilt calculation significantly decreases the amount of imaging used during THA by the direct anterior approach. Although it was a statistically significant reduction, both groups were within safe limits for both the surgical team and the patients, and thus the clinical significance is unknown. [Orthopedics.2016; 39(5):e962-e966.].
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".