Bibliographic record
Abstract
Commentary Somewhere between 20% and 33% of people will sustain an osteoporotic fracture during their lifetime1. Preventative treatment has mainly been antiresorptive therapy, such as bisphosphonates. These medications have reduced the risk of fractures in large clinical trials2,3. Unfortunately, prolonged use of bisphosphonates is associated with relatively rare but potentially troublesome atypical femur fractures (AFFs)4,5. We are just beginning to understand the clinical scenario of AFFs and develop patient treatment plans. The occurrence of AFFs is still relatively rare for bisphosphonate users6,7. Lim et al. have now evaluated 109 AFFs in 99 consecutive patients who had been operatively treated for complete AFFs. AFFs were defined by characteristic radiographic features, including a transverse or short oblique fracture line, medial spike, focal lateral cortical thickening, and no or minimal comminution, according to the criteria of a 2013 American Society for Bone and Mineral Research task force4. All of the patients had a documented history of bisphosphonate therapy, with an average duration of 7.4 years. Baseline demographic data, characteristics of the fracture and surgery, and radiographic findings including femoral neck-shaft angle, coronal and sagittal bowing of the femur, and the thickness of the femoral cortex were examined in this cohort. Lim et al. correctly point out that although the precise prognosis is still unknown, there is a growing consensus that the altered bone metabolism caused by long-term use of bisphosphonates would adversely affect bone-healing even after osteosynthesis. Delayed or failed fracture-healing could be a major concern after fracture stabilization, especially if patients continue to take bisphosphonates. Two or more of these antiresorptive drugs had been sequentially used in just over 10% of patients. Only 6 patients took a drug holiday from bisphosphonates for at least 1 year. Prodromal symptoms developed in 31 fractures, and the mean duration of symptoms (and standard deviation) was 7.0 ± 8.5 months. All patients discontinued bisphosphonate medications at the time of admission. Most (70%) of the fractures in the current study showed osseous union within 6 months after surgery. The remaining 30% of fractures revealed delayed union or nonunion and were assigned to the problematic healing group. This group had differences in body mass index (BMI), bisphosphonate medication duration, and the occurrence of prodromal symptoms compared with the patients who healed within 6 months. Iatrogenic cortical breakage around the fracture site as well as a ratio of the remaining gap to cortical thickness that was ≥0.2 on the anterior and lateral sides of the fracture site were surgeon-controllable factors associated with the problematic healing. (Mismatch between the femur and the nail geometry may be the cause of this.) The problem is that many patients have risk factors that are not surgeon-controllable; a varus neck may lead to an AFF, and femoral bowing is related to nonunion. From the reported results, it appears that left-sided fractures just do not heal properly, reflecting the limited sample size! Lim et al. believe that higher BMI, longer bisphosphonate duration, and the presence of prodromal symptoms yielded adverse effects on fracture-healing. Although it appears that prolonged use of bisphosphonates and the other 2 factors may be an issue, the difference is not significant when statistical correction for repeated measures is taken into consideration. The only significant difference that I can see is that these fractures do not heal as well as historical controls―especially those that occur in the subtrochanteric region. It is not hard to understand that these are pathological fractures and need to be treated as such. Most trauma surgeons use cephalomedullary nails to treat these fractures, but it is hard to determine from this manuscript whether there was an accepted gold standard. Certainly nailing in distraction, use of cerclage wires, or malreduction are not desirable surgical plans. It is impossible from this manuscript to determine what effect the fixation technique had on the outcomes. A larger study with multiple centers and a standardized therapy plan (including both medications and hardware) plus bone biopsies is the only way to further clarify solutions to this problem.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.023 | 0.015 |
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".