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Record W1997490888 · doi:10.1007/s11999-013-3401-0

CORR Insights®: Are Race and Sex Assessment Associated With the Occurrence of Atypical Femoral Fractures?

2013· letter· en· W1997490888 on OpenAlexaboutno aff
Joseph M. Lane

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2013
Typeletter
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBisphosphonateOsteoporosisEtiologySurgeryFemoral fractureFemoral shaftBone Density Conservation AgentsFemurInternal medicineBone mineral

Abstract

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Where Are We Now? The introduction of bisphosphonates to osteoporosis treatment is associated with a decrease in vertebral fractures by 70%, hip fractures by 40% to 50%, and peripheral fractures by 20% [2]. However, bisphosphonates are also associated with atypical femoral fractures, which can cause substantial morbidity; they present with minimal trauma in the subtrochanteric and femoral shaft, and they have a specific radiologic appearance. These fractures often are associated with prolonged (exceeding 5 years) bisphosphonate treatment. To address this problem, the Association for Bone and Mineral Research established a task force and released two reports [4, 5] that created criteria for atypical femoral fractures, while also addressing the risk and pathophysiology of these injuries. The task force noted that several groups found associations between atypical femoral fractures and bisphosphonates [4, 5]. The relative risk of prolonged bisphosphonate use is high for these fractures, and increases with duration. The absolute atypical femoral fracture risk is low, ranging from 3.2 to 50 cases per 100,000 person years. However, the rate increases to 100 to 200 cases per 100,000 person years for individuals whose treatment reaches 8 years. While the association has been established, causality has not been proven by the Bradford-Hill criteria [3]. A temporal relationship exists between bisphosphonate and atypical femoral fractures. The cessation of bisphosphonate treatment lowers the risk of atypical femoral fractures. However, the task force recognized that causality remains unresolved, and additional antiresorptive agents may have the same association. The exact etiology of the atypical femoral fractures has not been determined, and at-risk patients have yet to be identified. Where Do We Need To Go? Currently, issues of causality, etiology, and risk remain unsettled for atypical femoral fractures. The contribution of bisphosphonates and other anticatabolic agents to the generation of atypical femoral fractures requires a thorough understanding of the pathophysiology. Bisphosphonate primarily decreases bone resorption and secondarily bone formation. Several theories suggest an atypical femoral fracture's pathogenesis starts with low bone turnover and would include microdamage accumulation, reduced bone matrix heterogeneity, accumulation of advanced glycation end-products that alter collagen strength, crack propagation, loss of bone toughness, and unique bone geometry [4, 5]. Risk issues are more fully developed. Although an association with bisphosphonates and its duration has been established, most individuals utilizing long-term bisphosphonates do not experience atypical femoral fractures. It is worth noting that these drugs have markedly lowered the risk of osteoporotic fractures. Several registries [1, 4, 5] have reported that Asians may have an increased risk. Marcano and colleagues strongly support this view, and also call attention to Hispanics, who be at higher risk as well. None of the investigators have information on drug compliance, confounding diseases, or bone geometry to explain this added risk. There is a pressing need to establish clear criteria for the patient that requires treatment, the choice of treatment and duration, and identification of those individuals at risk for adverse events, including atypical femoral fractures. How Do We Get There? There are several avenues that may help us better understand atypical femoral fractures. Prospective hip and femoral fracture registries must be established, and should be coupled with osteoporosis registries to identify the potential risk and etiologic factors. No institution has a sufficient amount of atypical femoral fracture cases to resolve these issues. Therefore, large multiinstitutional cooperative efforts are required, similar to The Kaiser Permanente and Ontario cooperative efforts. The critical clinical, laboratory, radiologic, and historical data points need to be universally established for these investigations. As novel agents are introduced, how do they relate to atypical fractures? Are there drugs that actually rescue the low turnover bone? What drugs are preferred in the setting of the atypical femoral fractures? Clinical trials addressing these questions are required. There is a need for detailed structural analysis of individuals on long-term bisphosphonate therapy with and without atypical femoral fractures. Additionally, bone biopsies of the involved femurs would be helpful, particularly from the site of the fractures. There are several laboratories that have the special techniques required, including Fourier transform infrared imaging, microindentation, glycation product determination, and bone histomorphometry. Collaborative programs are needed to match the biopsy with these special laboratories. Detailed standardized data sets on the patients should accompany the investigations. Major clinical groups overseeing osteoporosis should provide some guidelines on how clinicians approach a potential drug holiday. Osteoporotic drug use has steadily declined during the last 5 years despite the unequivocal evidence for their efficacy in preventing fractures. Clear recommendations regarding treatment are needed for the clinicians by the bone biology community.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.099
Threshold uncertainty score0.330

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0990.014

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.

Opus teacher head0.082
GPT teacher head0.431
Teacher spread0.348 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreCommentary

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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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