Incidence and Characteristics of Atypical Femoral Fractures: Clinical and Geometrical Data
Bibliographic record
Abstract
Despite the multitude of studies published on atypical femoral fractures (AFFs), a profile for patients at risk does not exist. This study aimed first at estimating AFF incidence over a 19-month-period in Quebec City using the ASBMR Task force criteria to define AFF. The medical records of patients hospitalized for hip or femoral fracture between June 1, 2009, and December 31, 2010, were reviewed. Thirty-six cases of atypical fractures were identified during the 19-month period, representing an AFF incidence of 7.0 (range, 4.7 to 9.3) cases per 100,000 person-years. In the second part of the study, data regarding the characteristics suspected of increasing the risks of AFF were collected from medical and pharmacological records, proximal femur radiographs, and patient interviews. The data regarding each patient with an AFF during years 2008-2011 were compared to two controls with a hip or femoral fragility fracture or a traumatic fracture, paired for age and sex. Twenty patients with AFF were added to the 36 patients with AFF selected in the first part, thereby 56 patients with AFF were investigated. The association between the occurrence of AFF and bisphosphonates (BPs) use was proven statistically significant in multivariate analysis, odds ratio (OR) = 10.39 (95% CI, 2.22 to 48.58; p = 0.0029). Compared to controls, patients with AFF had excessive femoral offset (43.1 mm versus 38.3 mm, p = 0.0007), proximal femoral neck angle in varus (128.9 degrees versus 134.0 degrees, p < 0.0001), and had greater proximal cortical thickness. This retrospective study confirms the low incidence of AFF, confirms its significant association with exposure to BPs, and reveals the possible contribution of proximal femoral geometry in AFF occurrence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.002 | 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".