Clinical outcomes and response to treatment of patients receiving topical treatments for pyoderma gangrenosum: a prospective cohort study

Background: pyoderma gangrenosum (PG) is an uncommon dermatosis with a limited evidence base for treatment. Objective: to estimate the effectiveness of topical therapies in the treatment of PG. Methods: prospective cohort study of UK secondary care patients with a clinical diagnosis of PG suitable for topical treatment (recruited July 2009 to June 2012). Participants received topical therapy following normal clinical practice (mainly Class I-III topical corticosteroids, tacrolimus 0.03% or 0.1%). Primary outcome: speed of healing at 6 weeks. Secondary outcomes: proportion healed by 6 months; time to healing; global assessment; inflammation; pain; quality-of-life; treatment failure and recurrence. Results: Sixty-six patients (22 to 85 years) were enrolled. Clobetasol propionate 0.05% was the most commonly prescribed therapy. Overall, 28/66 (43.8%) of ulcers healed by 6 months. Median time-to-healing was 145 days (95% CI: 96 days, ∞). Initial ulcer size was a significant predictor of time-to-healing (hazard ratio 0.94 (0.88;80 1.00); p = 0.043). Four patients (15%) had a recurrence. Limitations: No randomised comparator Conclusion: Topical therapy is potentially an effective first-line treatment for PG that avoids possible side effects associated with systemic therapy. It remains unclear whether more severe disease will respond adequately to topical therapy alone

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License