Management of Necrotizing Pneumonia and Pulmonary Gangrene: A Case Series and Review of the Literature
Bibliographic record
Abstract
BACKGROUND: Necrotizing pneumonia is an uncommon but severe complication of bacterial pneumonia, associated with high morbidity and mortality. The availability of current data regarding the management of necrotizing pneumonia is limited to case reports and small retrospective observational cohort studies. Consequently, appropriate management for these patients remains unclear. OBJECTIVE: To describe five cases and review the available literature to help guide management of necrotizing pneumonia. METHODS: Cases involving five adults with respiratory failure due to necrotizing pneumonia admitted to a tertiary care centre and infected with Streptococcus pneumoniae (n=3), Klebsiella pneumoniae (n=1) and methicillin-resistant Staphylococcus aureus (n=1) were reviewed. All available literature was reviewed and encompassed case reports and retrospective reviews dating from 1975 to the present. RESULTS: All five patients received aggressive medical management and consultation by thoracic surgery. Three patients underwent surgical procedures to debride necrotic lung parenchyma. Two of the five patients died in hospital. CONCLUSIONS: Necrotizing pneumonia often leads to pulmonary gangrene. Computed tomography of the thorax with contrast is recommended to evaluate the pulmonary vascular supply. Further study is necessary to determine whether surgical intervention, in the absence of pulmonary gangrene, results in better outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".