Timing of surgical intervention for compartment syndrome in different body region: systematic review of the literature
Bibliographic record
Abstract
Compartment syndrome can occur in many body regions and may range from homeostasis asymptomatic alterations to severe, life-threatening conditions. Surgical intervention to decompress affected organs or area of the body is often the only effective treatment, although evidences to assess the best timing of intervention are lacking. Present paper systematically reviewed the literature stratifying timings according to the compartmental syndromes which may beneficiate from immediate, early, delayed, or prophylactic surgical decompression. Timing of decompression have been stratified into four categories: (1) immediate decompression for those compartmental syndromes whose missed therapy would rapidly lead to patient death or extreme disability, (2) early decompression with the time burden of 3-12 h and in any case before clinical signs of irreversible deterioration, (3) delayed decompression identified with decompression performed after 12 h or after signs of clinical deterioration has occurred, and (4) prophylactic decompression in those situations where high incidence of compartment syndrome is expected after a specific causative event.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".