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Record W3151248387 · doi:10.1093/jbcr/irab032.313

667 Necrotizing Soft Tissue Infection Within 48 Hours Post-Scald Burn: A Rare Presentation

2021· article· en· W3151248387 on OpenAlexaff
Luis E Meza, Sarah Rehou, Courtney H Grotski, Shahriar Shahrohki

Bibliographic record

VenueJournal of Burn Care & Research · 2021
Typearticle
Languageen
FieldMedicine
TopicStreptococcal Infections and Treatments
Canadian institutionsHealth Sciences CentreUniversity of TorontoUniversity of AlbertaSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineSurgerySoft tissueDebridement (dental)ResuscitationContext (archaeology)Necrotic tissueFasciotomySepsisAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

Abstract Introduction We report a case of a patient with a burn injury who developed a devastating necrotizing soft tissue infection (NSTI) early in the post-burn period. Methods An elderly male was admitted to an ABA verified burn centre after sustaining a 20% scald burn to his back and right upper extremity. He was found in the bathtub; a fall was suspected based on his history of Parkinson’s disease and a finding of bruising to his bilateral knees. Initially, his hospital course was uneventful apart from an elevated creatine kinase, which decreased with adequate resuscitation without signs or symptoms of compartment syndrome. Thirty-six hours following his admission, he developed rapid onset of progressively worsening renal function, respiratory requiring intubation, mechanical ventilation, and circulatory failure requiring vasopressor support. After ruling out other causes of shock and upon re-examination of his burns there were clinical signs of a rapidly advancing necrotizing soft tissue infection. He was taken urgently to the operating room for aggressive debridement of nonviable tissue. He underwent a right shoulder disarticulation and extensive debridement of the right chest, abdomen, and back. Intra-operative tissue samples and preoperative blood cultures were positive for Group A Streptococcus. The patient was predicted to require multiple operations and a prolonged hospital stay. Despite these interventions, his prognosis was poor. The family and the treatment team, in the context of the patient’s previous independent functioning, revised his goals of care on his first post-operative day. Life-sustaining treatment was withdrawn, and comfort care measures were implemented. The patient passed away two days later. Results We report a case of a patient with a burn injury who developed a devastating NSTI early in the post-burn period within 36–48 hours of presentation to a burn center. Soft-tissue infections in the immediate post-burn period are rare unless there is subsequent contamination. Burned tissue contains a large amount of necrotic tissue and protein-rich wound exudate, which provides a rich growth medium for bacteria. This, in addition to the immunosuppression secondary to the burn insult, favors the development of infection. NSTI in the context of thermal injury is a rare phenomenon and in the few reported cases in burn patients, necrotizing infections occurred closer to two weeks following the initial injury. Conclusions Necrotizing soft tissue infections are entities with a rapid and devastating course. The diagnosis is challenging, and occlusive dressings may contribute to a delay in diagnosis in burns. Acute hemodynamic compromise without any obvious cause should raise the suspicion for a necrotizing soft tissue infection and lead to early exposure of wounds in burn patients.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.059
GPT teacher head0.422
Teacher spread0.363 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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".

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Citations1
Published2021
Admission routes1
Has abstractyes

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