Limitations of conventional radiographs in the assessment of acetabular defects following total hip arthroplasty
Bibliographic record
Abstract
BACKGROUND: Conventional radiographs are routinely used to evaluate acetabular bone loss as part of the follow-up in patients who undergo total hip arthroplasty (THA). The objective of this study was to examine the accuracy and specificity of conventional radiographs reviewed by arthroplasty surgeons in detecting acetabular bone loss in patients with prior THA. METHODS: Using a cadaveric pelvic model, a defined percentage of bone was incrementally removed from the posterior acetabular column, followed by implantation of uncemented cups into both acetabula. Ten orthopedic arthroplasty surgeons, blinded to the defect sizes, assessed the percentage of bone defect using standard anteroposterior, Judet and oblique conventional radiographs. RESULTS: Observers were unable to accurately grade bone defects using conventional radiographs. For defects less than 50%, observers reported on average a defect of 11%. Although observer estimates of defects 50% or more increased, these treatment-altering bone deficiencies remained grossly underestimated, with a sensitivity and specificity of 36.6% and 97.6%, respectively. CONCLUSION: Conventional radiographs reviewed by experienced arthroplasty surgeons do not reliably detect small bone lesions (< 50%). Although more successful in detecting larger bone lesions, surgeons tend to underestimate actual bone loss. Computed tomography scanning may be indicated if accurate estimation of acetabular bone loss is required in patients who have undergone previous THA.
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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 | 0.020 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".