Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Autologous chondrocyte implantation (ACI) for the treatment of large and complex cartilage lesions of the knee

BACKGROUND: Complex cartilage lesions of the knee including large cartilage defects, kissing lesions, and osteoarthritis (OA) represent a common problem in orthopaedic surgery and a challenging task for the orthopaedic surgeon. As there is only limited data, we performed a prospective clinical study to investigate the benefit of autologous chondrocyte implantation (ACI) for this demanding patient population. METHODS: Fifty-one patients displaying at least one of the criteria were included in the present retrospective study: (1.) defect size larger than 10 cm2; (2.) multiple lesions; (3.) kissing lesions, cartilage lesions Outerbridge grade III-IV, and/or (4.) mild/moderate osteoarthritis (OA). For outcome measurements, the International Cartilage Society's International Knee Documentation Committee's (IKDC) questionnaire, as well as the Cincinnati, Tegner, Lysholm and Noyes scores were used. Radiographic evaluation for OA was done using the Kellgren score. RESULTS AND DISCUSSION: Patient's age was 36 years (13-61), defects size 7.25 (3-17.5) cm2, previous surgical procedures 1.94 (0-8), and follow-up 30 (12-63) months. Instruments for outcome measurement indicated significant improvement in activity, working ability, and sports. Mean ICRS grade improved from 3.8 preoperatively to grade 3 postoperatively, Tegner grade 1.4 enhanced to grade 3.39. The Cincinnati score enhanced from 25.65 to 66.33, the Lysholm score from 33.26 to 64.68, the Larson score from 43.59 to 79.31, and Noyes score from 12.5 to 46.67, representing an improvement from Cincinnati grade 3.65 to grade 2.1. Lysholm grade 4 improved to grade 3.33, and Larson grade 3.96 to 2.78 (Table 1), (p < 0.001). Patients with kissing cartilage lesions had similar results as patients with single cartilage lesions. CONCLUSION: Our results suggest that ACI provides mid-term results in patients with complex cartilage lesions of the knee. If long term results will confirm our findings, ACI may be a considered as a valuable tool for the treatment of complex cartilage lesions of the knee

Autologous chondrocyte implantation versus ACI using 3D-bioresorbable graft for the treatment of large full-thickness cartilage lesions of the knee

BACKGROUND: In autologous chondrocyte implantation (ACI), the periosteum patch which is sutured over the cartilage defect has been identified as a major source of complications such as periosteal hypertrophy. In the present retrospective study, we compared midterm results of first-generation ACI with a periosteal patch to second generation ACI using a biodegradable collagen fleece (BioSeed-C) in 82 patients suffering from chronic posttraumatic and degenerative cartilage lesions of the knee. METHODS: Clinical outcome was assessed in 42 patients of group 1 and in 40 patients of group 2 before implantation of the autologous chondrocytes and at a minimum follow-up of 2 years using the ICRS score, the modified Cincinnati score and the Lysholm score. RESULTS: Although patients treated with BioSeed-C had more previous surgical procedures on their respective knees, highly significant improvements (P < 0.001) were assessed in both groups at comparable outcome levels: the ICRS score improved from grade D (poor) preoperatively to grade C (fair); the modified Cincinnati knee score from 3.26 to 6.4 (group 1) and 3.3 and 6.88 (group 2). Lysholm score improved from 33 to 70 points (group 1) and from 47 to 78 points (group 2), respectively. Revision surgery was due to symptomatic periosteal hypertrophy (n = 4), graft failure (n = 3), plica syndrome (n = 2) synovectomy (n = 1) (group 1); and graft failure (n = 2), debridement (n = 1), synovectomy (n = 2) (group 2). CONCLUSION: These results suggest that BioSeed-C is an equally effective treatment option for focal degenerative chondral lesions of the knee in this challenging and complex patient profile

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press