Interobserver variation in predicting fracture risk in patients with long bone metastatic lesions.
Bibliographic record
Abstract
e20524 Background: Metastatic bone disease (MBD) is the most common malignant process affecting the long bones, with pathologic fractures carrying a large burden for patients. In 1989, Mirels proposed a scoring system to predict fracture risk in patients with MBD. Subsequent studies have shown low specificity of Mirels’ criteria with potential for unnecessary surgery and morbidity. The objective of this study was to determine the inter-observer reliability of Mirels’ criteria, subjective fracture risk prediction and first-line treatment. Methods: We surveyed 15 orthopaedic surgeons, 11 orthopaedic oncology surgeons, 3 radiologists and 5 radiation oncologists. Participants were asked to review 7 clinical cases and assess Mirels’ criteria, risk of pathologic fracture, and make treatment recommendations. They were blinded to treatment and outcomes. To assess inter-rater reliability, Krippendorrf's alpha (Kα) values were calculated. Kα values of >0.667 indicates sufficient level reliability while Kα values over >0.800 indicates good reliability. Results: With respect to lesion site, overall inter-rater reliability was sufficient at 0.75 (95% CI: 0.67-0.83), but low for the other 3 Mirels’ criteria: lesion appearance (0.45, 95% CI: 0.32-0.58), axial diameter (0.47, 95% CI: 0.36-0.58) and pain (0.18, 95% CI: -0.01-0.36). When asked to predict pathologic fracture risk, overall reliability was sufficient at 0.68 (95% CI: 0.62-0.75); highest among radiologists (0.89, 95% CI: 0.81-0.96) and lowest among orthopaedic oncology surgeons (0.60, 95% CI: 0.52-0.67). Conversely, overall reliability regarding first-line treatment was considerably lower at 0.16 (95% CI: 0.09-0.22); highest among radiation oncologists (0.42, 95% CI: 0.16-0.64). Surgery and radiation were the most frequent first-line treatment options. Conclusions: The results of this survey show that the reliability of Mirels’ criteria, “expert” opinion on fracture risk, and subsequent treatment for patients with MBD is less than ideal. These results highlight a need for future tools to accurately identify reliable clinical and radiographic variables that are most predictive of fracture risk and guide appropriate treatment.
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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 | 0.025 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".