The effect of teriparatide on patients with atypical femur fractures: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Bisphosphonates (BPs) are one of the most often used drugs to lower fracture risk in osteoporosis patients; nonetheless, BPs have been linked to atypical femoral fracture (AFF). Teriparatide (TPTD) is a parathyroid hormone analogue and anabolic drug that may accelerate fracture repair. TPTD has been considered as a possible treatment for AFF, particularly those caused by BP use. We evaluate the effect of TPTD on AFF in this systematic review and meta-analysis. MATERIALS AND METHODS: A thorough search of: Web of Science, Scopus, PubMed, and Cochrane was conducted on August 2, 2023. Trials evaluating the effect of TPTD on the incidence of: complete bone healing, non-union, early and delayed bone union, progression of incomplete AFF to complete AFF, and time to bone union were included. Using Review Manager (RevMan) version 5.4, the risk ratio (RR) and mean difference (MD) with the corresponding 95% confidence interval (CI) were estimated for dichotomous and continuous outcomes, respectively. The Newcastle-Ottawa Scale was used to assess the quality of studies. RESULTS: Eight studies met the eligibility criteria and were included in our analysis. TPTD significantly increased the incidence of early bone union (RR = 1.45, 95% CI [1.13, 1.87], P = 0.004) and time to bone union (MD = -1.56, 95% CI [-2.86, -0.26], P = 0.02) compared to the control group. No significant differences were observed in terms of complete bone healing (RR = 1.09, 95% CI [0.99, 1.13], P = 0.12), non-union (RR = 0.48, 95% CI [0.22, 1.04], P = 0.06), and progression of incomplete AFF to complete AFF (RR = 0.27, 95% CI [0.04, 1.97], P = 0.19). CONCLUSIONS: TPTD is an effective therapy for enhancing and hastening healing following AFF, particularly in postoperative settings. Future large randomized clinical trials are needed to confirm or dispute the results.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".