Ovarian sex cord stromal tumors: an institutional experience

Background: Sex cord stromal tumors are a heterogeneous group of rare neoplasms of ovary. These tumors arise from different cells of the ovary and have a fascinating variety of clinical presentations. They are mostly diagnosed on histopathology after surgical removal.Methods: Our study aims at discussing the clinical and histomorphological spectrum of these rare tumors at a tertiary care centre.Results: In our study 158 ovarian sex cord stromal tumors were received over a period of eight years at our institute. Out of these, the most common age group was 30 to 40 years and the chief complaint was abdominal pain and lump in majority of cases. Most common tumor histologically was Adult Granulosa cell tumor (42.4%). There were 8 (5.1%) Juvenile granulosa cell tumors, 31 (19.6%) fibromas, 6 (3.8%) thecomas, 14 (8.9%) fibrothecomas, 24 (15.2%) sertoli leydig cell tumors and 7 (4.4%) sclerosing stromal tumors. We encountered one case of sex cord tumor with annular tubules.Conclusions: Sex cord stromal tumors are uncommon ovarian tumors in Indians but have a wide range of distribution of age, clinical features and histopathological types. Since most of these have a relatively good prognosis, a high index of suspicion and thorough knowledge of clinicopathological findings is important for correct diagnosis and appropriate treatment

Assessment in early years education

Assessment is an integral part of the education process. For most of us the word ‘assessment’ conjures images of an examination hall, marks and report card, and the look of dissatisfaction on the face of the elders as our marks often never matched the expectation they had of us. Fear and insecurity would perhaps be the emotions most commonly associated with the word ‘assessment’. This picture of assessment is a consequence of a product oriented approach to education which sees education as the means to slot a large number of individuals into a few neat categories – bright, average, dull or intelligent, average, failure. These labels, awarded early, become self-fulfilling and stick with the individual for the rest of her life, influencing her approach to tasks even when she is out of the education system, so to say. Education, instead of being a process of empowerment, divests the individual of self confidence and self esteem. And this process begins right from the early years education in a pre-school

SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking

In-context learning with Large Language Models (LLMs) has emerged as a promising avenue of research in Dialog State Tracking (DST). However, the best-performing in-context learning methods involve retrieving and adding similar examples to the prompt, requiring access to labeled training data. Procuring such training data for a wide range of domains and applications is time-consuming, expensive, and, at times, infeasible. While zero-shot learning requires no training data, it significantly lags behind the few-shot setup. Thus, `\textit{Can we efficiently generate synthetic data for any dialogue schema to enable few-shot prompting?}' Addressing this question, we propose \method, a data generation framework tailored for DST, utilizing LLMs. Our approach only requires the dialogue schema and a few hand-crafted dialogue templates to synthesize natural, coherent, and free-flowing dialogues with DST annotations. Few-shot learning using data from {\method} results in $4-5$ improvement in Joint Goal Accuracy over the zero-shot baseline on MultiWOZ 2.1 and 2.4. Remarkably, our few-shot learning approach recovers nearly $98$ of the performance compared to the few-shot setup using human-annotated training data. Our synthetic data and code can be accessed at https://github.com/apple/ml-synthdstComment: 9 pages. 4 figures, EACL 2024 main conferenc

Referring to Screen Texts with Voice Assistants

Voice assistants help users make phone calls, send messages, create events, navigate, and do a lot more. However, assistants have limited capacity to understand their users' context. In this work, we aim to take a step in this direction. Our work dives into a new experience for users to refer to phone numbers, addresses, email addresses, URLs, and dates on their phone screens. Our focus lies in reference understanding, which becomes particularly interesting when multiple similar texts are present on screen, similar to visual grounding. We collect a dataset and propose a lightweight general-purpose model for this novel experience. Due to the high cost of consuming pixels directly, our system is designed to rely on the extracted text from the UI. Our model is modular, thus offering flexibility, improved interpretability, and efficient runtime memory utilization.Comment: 7 pages, Accepted to ACL Industry Track 202

Risk factors for extubation failure in mechanically ventilated children in pediatric intensive care unit

Background: Mechanical ventilation is lifesaving in children with respiratory failure. However, its unnecessary prolongation once a child is capable of sustaining spontaneous effective ventilation is associated with significant complications. Objective: To identify the factors that lead to higher chance of extubation failure in mechanically ventilated children. Materials and Methods: A prospective, observational study was conducted over a period of 1 year. Children admitted to pediatric intensive care unit of a tertiary care hospital of Northern India aged 1 month–17 years, needing mechanical ventilation were included in the study. Predefined criteria were used to decide the timing of extubation. Relevant details were recorded to study various patient-related parameters and their association with extubation outcome. Results: Mean age of the study group was 50 months with a male:female ratio of 3:1. Extubation failure rate was 14.5%. Extubation failure was significantly higher in patients ventilated for >7 days (p=0.01), those with the pediatric risk of mortality score >10 at admission (p=0.009), in addition to peak inspiratory pressure >16 cm H2O (p=0.009) and FiO2 ≥0.35 (p=0.01) before extubation. Accidental extubation was also associated with higher extubation failure (p<0.001). Conclusion: Our study demonstrates that even though sicker patients requiring ventilation for longer duration are more prone to failed extubations, protocol based, and planned extubations lead to better extubation success

Effective Long-Context Scaling of Foundation Models

We present a series of long-context LLMs that support effective context windows of up to 32,768 tokens. Our model series are built through continual pretraining from Llama 2 with longer training sequences and on a dataset where long texts are upsampled. We perform extensive evaluation on language modeling, synthetic context probing tasks, and a wide range of research benchmarks. On research benchmarks, our models achieve consistent improvements on most regular tasks and significant improvements on long-context tasks over Llama 2. Notably, with a cost-effective instruction tuning procedure that does not require human-annotated long instruction data, the 70B variant can already surpass gpt-3.5-turbo-16k's overall performance on a suite of long-context tasks. Alongside these results, we provide an in-depth analysis on the individual components of our method. We delve into Llama's position encodings and discuss its limitation in modeling long dependencies. We also examine the impact of various design choices in the pretraining process, including the data mix and the training curriculum of sequence lengths -- our ablation experiments suggest that having abundant long texts in the pretrain dataset is not the key to achieving strong performance, and we empirically verify that long context continual pretraining is more efficient and similarly effective compared to pretraining from scratch with long sequences