Surgical and audiological outcome of canal wall down mastoidectomy in Sub Himalayan region: our experience

Background: Pre-operative and post-operative hearing status and status of mastoid cavity were compared in patients undergoing canal wall down mastoidectomy (CWDM) with tympanoplasty.Methods: Forty-three patients who underwent surgery and completed their follow up post-surgery were included in the study. Nineteen patients underwent CWDM with type III tympanoplasty with PORP, 7 patients underwent CWDM with type III tympanoplasty without PORP and 17 patients underwent CWDM with type IV tympanoplasty with TORP.Results: Among enrolled patients, 21 patients were females and 22 patients were male. Right ear (29) was commonly involved than left ear (14). Hearing loss was predominant symptom followed by recurrent ear discharge and other symptoms. Patients underwent three types of surgeries, type III tympanoplasty with PORP (19/43), type III tympanoplasty without PORP (7/43) and type IV tympanoplasty with TORP (17/43) by using Teflon prosthesis.Conclusions: Thirty seven percent (16/43) of patients had hearing threshold 60dB hearing threshold, all belonging to group C. Anatomical results were assessed by examining the mastoid cavity showing 95%, 72%, 70% patients in group A, B and C had well epithelialized cavity

Comparative evaluation of FESS and septoplasty with FESS in cases of DNS with chronic maxillary sinusitis

Background: Present study compares basic FESS and septoplasty with FESS alone in DNS with maxillary sinusitis.Methods: Sixty patients of DNS with chronic maxillary sinusitis were divided into two groups alternatively. After pre-operative symptoms score and computerized tomography (CT scan), twenty patients underwent FESS with septoplasty (group A) and other 20 underwent FESS alone (group B) under local anaesthesia and topical 4% lignocaine with 1:1000 adrenaline. At 6 weeks, post-operative symptom score and CT scan findings were documented and compared statistically by using unpaired student t-test.Results: Ninety six percent of patients in group A and 87.6% in group B have shown complete improvement in facial pain/pressure. Ninety three percent of patients in group A and 83.3% in group B have shown complete improvement in headache. Ninety percent patients in group A and 63.3% in group B has shown complete improvement in nasal obstruction. Seventy six percent of patients in group A and 63.3% of patients in group B have shown complete improvement in nasal discharge. Eighty six percent and 63.3% of patients in group A and group B respectively were satisfied from the surgery. Ninety three percent of patients in group A and 70% in group B were found to have normal maxillary sinus mucosa on HRCT nose and PNS after 6 weeks following surgical treatment. Hundred percent patients in group A and 96.7% of patients in group B were found to have normal OMC on HRCT nose and PNS 6 weeks after surgery.Conclusions: It was observed that FESS with septoplasty is effective for the treatment of chronic rhinosinusitis with deviated nasal septum on VAS as well as radiologically (the Lund and Mackay staging system: radiologic staging) than FESS alone

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-benc