Return to Play After Hook of Hamate Fracture: A Systematic Review and Meta-Analysis.

In athletes, a hook of hamate fracture is concerning in terms of time to return to sport and effect on performance upon return. This study aims to analyze the treatment of hook of hamate fractures in athletes to determine their rates of return to play, timelines of recovery, and performance level upon return to play. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used to perform this analysis. The PubMed database was queried to perform the literature search. Data were pooled and analyzed. P values <.05 were considered significant. Data were analyzed using the Comprehensive Meta-Analysis software to determine heterogeneity. Twenty studies with 823 patients sustaining hook of hamate fractures that reported any competitive level of play were included in the analysis. Of the 823 patients, 778 (94.5%) were able to return to play with 91.2% (506/555) of patients demonstrating similar or improved performance. The mean time to return to play was 45 days (range: 21-168 days). Treatment included surgical excision for 787 patients (95.6%), open reduction and internal fixation for 18 patients (2.2%), stress reduction/casting for 13 patients (1.6%), and loss to follow-up or surgery refusal for 5 patients (0.6%). A very high number of athletes return to play following a hook of hamate fracture at the same or improved level of performance. In our study, the majority of injuries were treated with surgical excision of the fractured hook of hamate fragment. Most athletes returned to their sport at an average of 45 days.

Intraoperative embolectomy of a left illiac artery thrombus during an anterior lumbar interbody fusion.

Background: Anterior lumbar interbody fusion (ALIF) offers direct midline access to the lumbar intervertebral discs utilizing an anterior retroperitoneal approach. Here, a 33-year-old female undergoing ALIF developed an acute intraoperative left iliac artery thrombus and underwent immediate successful embolectomy. Case Description: A 33-year-old female was undergoing routine L5-S1 ALIF when an acute intraoperative left iliac vein injury occurred, requiring immediate repair by a vascular surgeon. Her left foot pulse oximeter showed a decreased reading, and her Doppler ultrasound confirmed reduced flow in the distal external iliac artery due to a thrombus. She required an immediate left iliac artery embolectomy, and flow was immediately restored. Postoperatively, she recovered well, and 3 months postoperatively, she remained neurologically intact without any further complications. Conclusion: Using pulse oximeters in patients undergoing ALIF surgery can aid in facilitating the diagnosis and treatment of acute artery thrombi. When such vascular injuries arise, having immediate access to experienced vascular surgeons is critical to obtain expeditious treatment and optimize patient outcomes.

Generalizability and portability of natural language processing system to extract individual social risk factors.

OBJECTIVE: The objective of this study is to validate and report on portability and generalizability of a Natural Language Processing (NLP) method to extract individual social factors from clinical notes, which was originally developed at a different institution. MATERIALS AND METHODS: A rule-based deterministic state machine NLP model was developed to extract financial insecurity and housing instability using notes from one institution and was applied on all notes written during 6 months at another institution. 10% of positively-classified notes by NLP and the same number of negatively-classified notes were manually annotated. The NLP model was adjusted to accommodate notes at the new site. Accuracy, positive predictive value, sensitivity, and specificity were calculated. RESULTS: More than 6 million notes were processed at the receiving site by the NLP model, which resulted in about 13,000 and 19,000 classified as positive for financial insecurity and housing instability, respectively. The NLP model showed excellent performance on the validation dataset with all measures over 0.87 for both social factors. DISCUSSION: Our study illustrated the need to accommodate institution-specific note-writing templates as well as clinical terminology of emergent diseases when applying NLP model for social factors. A state machine is relatively simple to port effectively across institutions. Our study. showed superior performance to similar generalizability studies for extracting social factors. CONCLUSION: Rule-based NLP model to extract social factors from clinical notes showed strong portability and generalizability across organizationally and geographically distinct institutions. With only relatively simple modifications, we obtained promising performance from an NLP-based model.