Purpose Elderly patients with intertrochanteric fractures exhibit post-traumatic hidden blood loss (HBL). This study aimed to evaluate the efficacy and safety of reducing post-traumatic HBL via early intravenous (IV) tranexamic acid (TXA) intervention in elderly patients with intertrochanteric fracture. Methods A prospective randomized controlled study was conducted with 125 patients (age ≥ 65 years, injury time ≤ 6 h) who presented with intertrochanteric fracture from September 2018 and September 2019. Patients in the TXA group (n = 63) received 1 g of IV TXA at admission, whereas those in the normal saline (NS) group (n = 62) received an equal volume of saline. Hemoglobin (Hgb) and hematocrit (Hct) were recorded at post-traumatic admission (PTA) and on post-traumatic days (PTDs) 1–3. HBL was calculated using the Gross formula. Lower extremity venous ultrasound was performed to detect venous thrombosis. Results Hgb on PTDs 2 and 3 was statistically higher in the TXA group than in the NS group. Hct and HBL on PTDs 1–3 were significantly less in the TXA group compared to the NS group. Preoperative transfusion rate was significantly lower in the TXA group compared with the NS group. There was no difference between the two groups with regard to the rates of complications. Conclusion Early IV TXA intervention could reduce post-traumatic HBL and pre-operative transfusion rate in elderly patients with intertrochanteric fractures without increasing the risk of venous thrombosis.

We present Dictionary Fields, a novel neural representation which decomposes a signal into a product of factors, each represented by a classical or neural field representation, operating on transformed input coordinates. More specifically, we factorize a signal into a coefficient field and a basis field, and exploit periodic coordinate transformations to apply the same basis functions across multiple locations and scales. Our experiments show that Dictionary Fields lead to improvements in approximation quality, compactness, and training time when compared to previous fast reconstruction methods. Experimentally, our representation achieves better image approximation quality on 2D image regression tasks, higher geometric quality when reconstructing 3D signed distance fields, and higher compactness for radiance field reconstruction tasks. Furthermore, Dictionary Fields enable generalization to unseen images/3D scenes by sharing bases across signals during training which greatly benefits use cases such as image regression from partial observations and few-shot radiance field reconstruction.

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