Neonatal and infant pulmonary thromboembolism
Bibliographic record
Abstract
Pulmonary thromboembolism (PTE) is rare in neonates and infants; however evidence suggests it is underdiagnosed. The primary objective is to conduct a scientific review to determine if the presentation, diagnosis, treatment and outcomes of neonates and infants with PTE are consistent across studies. Secondly, to develop an algorithm to establish the diagnosis and management of the condition based on current information. Two authors searched the literature independently using existing databases and verified that identical articles were assembled. Infants aged less than 1 year with PTE were included and further categorized into neonates 28 days or less and infants 29 days to 1 year or less. Forty-five articles with 157 cases (121 neonates; 36 infants) were identified with PTE. All of the reports were descriptive and neither randomized controlled trials nor prospective or case-control studies were identified. The reports are sub-classified into cases of pulmonary air embolism (PAE) with a higher mortality rate and patients with PTE. Diagnostic and treatment strategies varied widely and were individually case-based, dependent on clinical findings, which influenced patient outcomes. Scientific data to guide an evidence-based, diagnostic and treatment approach to PTE is limited because of the absence of rigorous clinical trials. Large scale, multicenter collaborative studies are required to firmly establish the management of PTE in this population.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".