Fat Embolism, Fat Embolism Syndrome and the Autopsy
Bibliographic record
Abstract
Fat embolism is common following trauma and is a common autopsy finding in these cases. It may also be seen in non-traumatic cases and is seen in children as well as adults. In comparison fat embolism syndrome (FES) only occurs in a small number of trauma and non-trauma cases. Clinical diagnosis is based on characteristic clinical and laboratory findings. Fat embolism exerts its effect by mechanical blockage of vessels and/or by biochemical means including breakdown of fat to free fatty acids causing an inflammatory response. Fat embolism can be identified at autopsy on microscopy of the lungs using fat stains conducted on frozen tissue, including on formalin fixed but not processed tissue. With FES fat emboli can be seen in other organs including the brain, kidney and myocardium. Fat can also be identified with post-fixation staining, typically with osmium tetroxide. Scoring systems have been developed to try and determine the severity of fat embolism in lung tissue. Fat embolism is also common following resuscitation. When no resuscitation has taken place, the presence of fat on lung histology has been used as proof of vitality. Diagnosis of fat embolism syndrome at autopsy requires analysis of the history, clinical and laboratory findings along with autopsy investigations to determine its relevance, but is an important diagnosis to make which is not always identified clinically. This paper reviews the history, clinical and laboratory findings and diagnosis of fat embolism and fat embolism syndrome at autopsy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 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".