This study evaluated the in vitro and in vivo performance of antibiotic-releasing porous polymethylmethacrylate (PMMA)-based space maintainers comprising a gelatin hydrogel porogen and a poly(DL-lactic-co-glycolic acid) (PLGA) particulate carrier for antibiotic delivery. Colistin was released in vitro from either gelatin or PLGA microparticle loaded PMMA constructs, with gelatin-loaded constructs releasing colistin over approximately 7 days and PLGA microparticle-loaded constructs releasing colistin up to 8 weeks. Three formulations with either a burst release or extended release in different doses were tested in a rabbit mandibular defect inoculated with Acinetobacter baumannii (2 × 10 7 colony forming units/mL). In addition, one material control that released antibiotic but was not inoculated with A. baumannii was tested. A. baumannii was not detectable in any animal after 12 weeks by culture of the defect, saliva, or blood. Defects with high-dose, extended-release implants had greater soft tissue healing compared to defects with burst release implants, with 8 out of 10 animals showing healed mucosae compared to 2 out of 10 with healed mucosae, respectively. Extended release of locally delivered colistin via a PLGA microparticle carrier improved soft tissue healing over the implants compared to burst release of colistin from a gelatin carrier.
Peripheral nerve blocks are commonly used in total knee arthroplasty (TKA). Liposomal bupivacaine is an extended-release anesthetic medication that maintains efficacy upwards of 72 hours. This study compared single-shot liposomal bupivacaine (LB) with the standard single-shot bupivacaine (SB) in a preoperative adductor canal block in TKA patients. A double-blind randomized, controlled trial at a single institution was performed in patients undergoing TKA. A standard preoperative single-shot adductor canal nerve injection was performed in 31 patients using 266 mg of liposomal bupivacaine (20 mL), whereas 32 patients received a standard formulation of 0.5% bupivacaine hydrogen chloride (20 mL). The primary outcome measure was postoperative gait velocity. Secondary outcomes included knee range of motion, pain scores, patient satisfaction, knee extension strength, opioid consumption, length of stay, and adverse events. There were no differences in baseline measures between groups. Improved pain ratings with activity (
P
=.009) were noted on postoperative day 1 with LB (mean, 4.4; SD, 2.0) compared with SB (mean, 5.9; SD, 2.6). Fewer opioids were used with LB compared with SB on postoperative day 1 (mean, 51.2 vs 66.1;
P
=.020) and on postoperative day 2 (mean, 39.5 vs 54.8;
P
=.016). No statistically significant differences in gait velocity, knee range of motion, knee extension strength, or patient satisfaction occurred. Peripheral nerve blockade with a single-shot adductor canal injection demonstrated improved pain scores with activity and diminished postoperative narcotic use when using LB compared with SB in TKA patients. There may be early postoperative advantages with LB as a single-shot injection in adductor canal blockade for patients undergoing TKA. [
Orthopedics
. 2021;44(4):249–255.]
BackgroundThe aim of this study was to validate a three-class sentiment classification model for clinical trial abstracts combining adversarial learning and the BioBERT language processing model as a tool to assess trends in biomedical literature in a clearly reproducible manner. We then assessed the model's performance for this application and compared it to previous models used for this task.MethodsUsing 108 expert-annotated clinical trial abstracts and 2,000 unlabeled abstracts this study develops a three-class sentiment classification algorithm for clinical trial abstracts. The model uses a semi-supervised model based on the Bidirectional Encoder Representation from Transformers (BERT) model, a much more advanced and accurate method compared to previously used models based upon traditional machine learning methods. The prediction performance was compared to those previous studies.ResultsThe algorithm was found to have a classification accuracy of 91.3%, with a macro F1-Score of 0.92, significantly outperforming previous studies used to classify sentiment in clinical trial literature, while also making the sentiment classification finer grained with greater reproducibility.ConclusionWe demonstrate an easily applied sentiment classification model for clinical trial abstracts that significantly outperforms previous models with greater reproducibility and applicability to large-scale study of reporting trends.
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