Bibliographic record
Abstract
Intrapulmonary hemorrhage is a rare, yet serious condition. The aim was to determine the frequency of Intrapulmonary hemorrhage in small vessel vasculitis and to find out factors associated with low mortality. Retrospective study charts of patients hospitalized at King Abdulaziz University Hospital, Jeddah between January 2000 and December 2010 were reviewed. The review included the underlying diseases, laboratory and radiological investigations, treatment modalities and mortality. Data analysis was performed using the Statistical Package for the Social Sciences. Among 36 patients with small vessel vasculitis were followed up during the ten-year period, six (16.7%) fitted the criteria for intrapulmonary hemorrhage. The underlying diseases were systemic lupus erythematosus (n=2; 33.3%), Goodpastures syndrome (n=1; 16.7%), Wegeners granulomatosis (n=1; 16.7%), and microscopic polyangiitis (n=1; 16.7%); hence, one patient was initially treated as a case of tuberculosis but was later found to have intrapulmonary hemorrhage. The incidence of intrapulmonary hemorrhage in patients with small vessel vasculitis was 16.7%, and the overall mortality rate was 33.3%. Further researches are warranted to explore the relation between mortality and current treatment options and the effect of underlying diseases on the outcome.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".