Bibliographic record
Abstract
There have been growing concerns about venous thromboembolism (VTE), especially in Western counties where the incidence is greater than Asian counties. In Korea, the annual incidence of VTE per 100,000 population in all age group, age group of 0–9 years and 10–19 years were 8.83, 0.19 and 0.71 in 2004, and increased to 13.8, 0.30 and 0.64 in 2008, respectively, showing significantly lower incidence of VTE in children and adolescents in comparison to adults [1]. Although the incidence of VTE is remarkably lower in children compared to adults, pediatric VTE is also gaining increased awareness because severe VTE may lead to serious morbidity and even death in pediatric patients as well. It has been demonstrated that pediatric VTE is an increasingly common complication among hospitalized children, now occurring in 42–58/10,000 pediatric admissions [2,3]; representing roughly a 10-fold increase over the original Canadian estimates from the early 1990s [4]. Of note, the majority of pediatric VTE occur in the tertiary care hospitals, where more intensive medical interventions with increased awareness and recognition can be provided [5].
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".