MicroRNAs in hepatocellular carcinoma – therapeutics and beyond: A systematic review
Bibliographic record
Abstract
IJS Short Reports is the first peer-reviewed, international, open access journal seeking to publish "negative" studies across the full breadth of the surgical field. We consider all forms of original research from case series to trials as well as non-confirmatory, unexpected, controversial and provocative results. We aim to provide rapid submission to decision times whilst maintaining a high-quality peer-review process that focusses on the quality of the article and the transparency of the reporting rather than the magnitude and direction of the results. The surgical community needs to know what works and what doesn't work in order to drive research in the correct direction, aid collaboration, prevent duplication and wasted resources. Other issues our community grapples with, is underpowered studies, poor statistical methods, poor reproducibility and external validity, poor methodology and reporting of studies. Publishing negative results brings the focus away from the results themselves to the research questions, the hypothesis and the robustness of the methodology used to investigate it. Such studies are all too often rejected by journals due to the direction of their results, rather than the quality of the methodology and the data and the contextual significance of the research questions they answer. IJS Short Reports is part of the IJS Publishing Group , a scientific publishing house established in 2003.
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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".