Additional file 1 of Comparative effectiveness and safety of DOACs vs. VKAs in treatment of left ventricular thrombus- a meta-analysis update
Bibliographic record
Abstract
Additional file 1: Supplementary Table 1. The detailed search strategy of databases. Supplementary Table 2. Quality assessment of included observational studies using the Newcastle-Ottawa Scale. Supplementary Figure 1. Quality assessment of the included randomized controlled trials. Supplementary Figure 2. Forest plot comparing the efficacy of DOACs and VKA in the treatment of LVT. (A) after 3 months of treatment. (B) after 6 months of treatment. Supplementary Figure 3. Forest plot to compare apixaban with VKAs in outcomes including (A) LVT resolution, (B) stoke/systemic embolism, (C) any bleeding. Supplementary Figure 4. Forest plot to compare rivaroxaban with VKAs in outcomes including (A) LVT resolution. (B) stroke. (C) stroke/systemic embolism. (D) major bleeding. (E) any bleeding. Supplementary Figure 5. The funnel plots based on the outcomes. (A) funnel plot for LVT resolution. (B) funnel plot for stroke. (C) funnel plot for systemic embolism. (D) funnel plot for stroke/systemic embolism. (E) funnel plot for all-cause mortality. (F) funnel plot for any bleeding. (G)funnel plot for major bleeding events. Supplementary Figure 6. Results of sensitivity analyses. (A) LVT resolution. (B) ischemic stroke. (C) systemic embolism. (D) stroke/systemic embolism. (E) any bleeding. (F) major bleeding. (G) intracranial hemorrhage. (H) all-cause mortality. (I) adverse cardiovascular events.
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 | 0.005 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.777 | 0.038 |
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".