Increase in non-evidence based use of antidepressants in children is cause for concern
Bibliographic record
Abstract
Introduction Ulcerative colitis (UC) is a type of inflammatory bowel disease, and 62% of patients with UC felt that it is difficult for them to live a normal life. Furthermore, some researches have shown that about 15% of patients with UC undergo at least one extreme clinical course in their lifetime, and 10%–30% of patients with UC oblige colectomy. Although many investigations have demonstrated that HBO2 has a beneficial impact on UC treatment, a systematic review and meta-analysis are unavailable. Therefore, a meta-analysis is essential to assess the efficacy and safety of HBO2 in treating UC. Methods and analysis A systematic search plan will be performed in the following seven databases with a restriction of time from inception to September 2020 to filter the eligible studies: PubMed, Web of Science, Embase, Cochrane Library, China National Knowledge Infrastructure, Chinese Scientific Journal Database (VIP) and Chinese Biomedical Database WanFang. Other related resources will be also searched. Two independent reviewers will choose eligible researches and extract data. The risk of bias will be evaluated based on Cochrane Collaboration’s Risk of Bias tool and Newcastle-Ottawa Scale. Eventually, a systematic review and meta-analysis will be performed via the Review Manager V.5.3 statistical software and STATA V.14.0 software. Ethics and dissemination This study will not involve the individual patient and any ethical problems since its outcomes are based on published data. Therefore, no ethical review and approval are required. We plan to publish the study in a peer-reviewed journal. PROSPERO registration number CRD42020210244.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".