Tiny Language Models Enriched with Multimodal Knowledge from Multiplex Networks

Large transformer language models trained exclusively on massive quantities of text are now the standard in NLP. In addition to the impractical amounts of data used to train them, they require enormous computational resources for training. Furthermore, they lack the rich array of sensory information available to humans, who can learn language with much less exposure to language. In this study, performed for submission in the BabyLM challenge, we show that we can improve a small transformer model’s data efficiency by enriching its embeddings by swapping the learned word embeddings from a tiny transformer model with vectors extracted from a custom multiplex network that encodes visual and sensorimotor information. Further, we use a custom variation of the ELECTRA model that contains less than 7 million parameters and can be trained end-to-end using a single GPU. Our experiments show that models using these embeddings outperform equivalent models when pretrained with only the small BabyLM dataset, containing only 10 million words of text, on a variety of natural language understanding tasks from the GLUE and SuperGLUE benchmarks and a variation of the BLiMP task

Radiographic alignment outcomes after the single-position prone transpsoas approach: a multi-institutional retrospective review of 363 cases

OBJECTIVE: The aim of this paper was to evaluate the changes in radiographic spinopelvic parameters in a large cohort of patients undergoing the prone transpsoas approach to the lumbar spine. METHODS: A multicenter retrospective observational cohort study was performed for all patients who underwent lateral lumber interbody fusion via the single-position prone transpsoas (PTP) approach. Spinopelvic parameters from preoperative and first upright postoperative radiographs were collected, including lumbar lordosis (LL), pelvic incidence (PI), and pelvic tilt (PT). Functional indices (visual analog scale score), and patient-reported outcomes (Oswestry Disability Index) were also recorded from pre- and postoperative appointments. RESULTS: Of the 363 patients who successfully underwent the procedure, LL after fusion was 50.0° compared with 45.6° preoperatively (p \u3c 0.001). The pelvic incidence-lumbar lordosis mismatch (PI-LL) was 10.5° preoperatively versus 2.9° postoperatively (p \u3c 0.001). PT did not significantly change (0.2° ± 10.7°, p \u3e 0.05). CONCLUSIONS: The PTP approach allows significant gain in lordotic augmentation, which was associated with good functional results at follow-up

Complications of the Prone Transpsoas Lateral Lumbar Interbody Fusion for Degenerative Lumbar Spine Disease: A Multicenter Study

BACKGROUND AND OBJECTIVES: The prone transpsoas (PTP) approach for lateral lumbar interbody fusion (LLIF) is a novel technique for degenerative lumbar spine disease. However, there is a paucity of information in the literature on the complications of this procedure, with all published data consisting of small samples. We aimed to report the intraoperative and postoperative complications of PTP in the largest study to date. METHODS: A retrospective electronic medical record review was conducted at 11 centers to identify consecutive patients who underwent LLIF through the PTP approach between January 1, 2021, and December 31, 2021. The following data were collected: intraoperative characteristics (operative time, estimated blood loss [EBL], intraoperative complications [anterior longitudinal ligament (ALL) rupture, cage subsidence, vascular and visceral injuries]), postoperative complications, and hospital stay. RESULTS: A total of 365 patients were included in the study. Among these patients, 2.2% had ALL rupture, 0.3% had cage subsidence, 0.3% had a vascular injury, 0.3% had a ureteric injury, and no other visceral injuries were reported. Mean operative time was 226.2 ± 147.9 minutes. Mean EBL was 138.4 ± 215.6 mL. Mean hospital stay was 2.7 ± 2.2 days. Postoperative complications included new sensory symptoms-8.2%, new lower extremity weakness-5.8%, wound infection-1.4%, cage subsidence-0.8%, psoas hematoma-0.5%, small bowel obstruction and ischemia-0.3%, and 90-day readmission-1.9%. CONCLUSION: In this multicenter case series, the PTP approach was well tolerated and associated with a satisfactory safety profile

International consensus statement on allergy and rhinology : rhinosinusitis

Background: The body of knowledge regarding rhinosinusitis (RS) continues to expand, with rapid growth in number of publications, yet substantial variability in the quality of those presentations. In an effort to both consolidate and critically appraise this information, rhinologic experts from around the world have produced the International Consensus Statement on Allergy and Rhinology: Rhinosinusitis(ICAR:RS). Methods: Evidence-based reviews with recommendations(EBRRs) were developed for scores of topics, using previously reported methodology. Where existing evidence was insufficient for an EBRR, an evidence-based review (EBR)was produced. The sections were then synthesized and the entire manuscript was then reviewed by all authors for consensus. Results: The resulting ICAR:RS document addresses multiple topics in RS, including acute RS (ARS), chronic RS (CRS)with and without nasal polyps (CRSwNP and CRSsNP), re-current acute RS (RARS), acute exacerbation of CRS (AE-CRS), and pediatric RS. Conclusion: As a critical review of the RS literature, ICAR:RS provides a thorough review of pathophysiology and evidence-based recommendations for medical and surgical treatment. It also demonstrates the significant gaps in our understanding of the pathophysiology and optimal management of RS. Too often the foundation upon which these recommendations are based is comprised of lower-level evidence. It is our hope that this summary of the evidence in RS will point out where additional research efforts may be directed.188 page(s

International Consensus Statement on Allergy and Rhinology: Rhinosinusitis

Isam Alobid, MD, PhD(1) , Nithin D. Adappa, MD(2) , Henry P. Barham, MD(3) , Thiago Bezerra, MD(4) , Nadieska Caballero, MD(5) , Eugene G. Chang, MD(6) , Gaurav Chawdhary, MD(7) , Philip Chen, MD(8) , John P. Dahl, MD, PhD(9) , Anthony Del Signore, MD(10) , Carrie Flanagan, MD(11) , Daniel N. Frank, PhD(12) , Kai Fruth, MD, PhD(13) , Anne Getz, MD(14) , Samuel Greig, MD(15) , Elisa A. Illing, MD(16) , David W. Jang, MD(17) , Yong Gi Jung, MD(18) , Sammy Khalili, MD, MSc(19) , Cristobal Langdon, MD(20) , Kent Lam, MD(21) , Stella Lee, MD(22) , Seth Lieberman, MD(23) , Patricia Loftus, MD(24) , Luis Macias-Valle, MD(25) , R. Peter Manes, MD(26) , Jill Mazza, MD(27) , Leandra Mfuna, MD(28) , David Morrissey, MD(29) , Sue Jean Mun, MD(30) , Jonathan B. Overdevest, MD, PhD(31) , Jayant M. Pinto, MD(32) , Jain Ravi, MD(33) , Douglas Reh, MD(34) , Peta L. Sacks, MD(35) , Michael H. Saste, MD(36) , John Schneider, MD, MA(37) , Ahmad R. Sedaghat, MD, PhD(38) , Zachary M. Soler, MD(39) , Neville Teo, MD(40) , Kota Wada, MD(41) , Kevin Welch, MD(42) , Troy D. Woodard, MD(43) , Alan Workman(44) , Yi Chen Zhao, MD(45) , David Zopf, MD(46) CONTRIBUTING AUTHOR AFFILIATIONS: (1) Universidad de Barcelona; (2) University of Pennsylvania; (3) Louisiana State University Health Sciences Center; (4) Universidade de São Paulo; (5) ENT Specialists of Illinois; (6) University of Arizona; (7) University of Oxford; (8) University of Texas; (9) University of Indiana; (10) Mount Sinai Beth Israel; (11) Emory University; (12) University of Colorado; (13) Wiesbaden, Germany; (14) University of Colorado; (15) University of Alberta; (16) University of Alabama at Birmingham; (17) Duke University; (18) Sungkyunkwan University; (19) University of Pennsylvania; (20) Universidad de Barcelona; (21) Northwestern University; (22) University of Pittsburgh; (23) New York University; (24) Emory University; (25) University of British Columbia; (26) Yale University School of Medicine; (27) Private Practice; (28) Department of Otolaryngology, Hôtel-Dieu Hospital, Centre de Recherche du Centre Hospitalier de l'Université de Montréal; (29) University of Adelaide; (30) Pusan National University; (31) University of California, San Francisco; (32) University of Chicago; (33) University of Auckland; (34) Johns Hopkins University; (35) University of New South Wales, Australia; (36) Stanford University; (37) Washington University; (38) Harvard Medical School; (39) Medical University of South Carolina; (40) Singapore General Hospital; (41) Taho University; (42) Northwestern University; (43) Cleveland Clinic Foundation; (44) University of Pennsylvania; (45) University of Adelaide; (46) University of Michigan.status: publishe