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Record W4390956397

Negative Pressure Wound Therapy in Surgical Site Infections of Sternotomy Wounds - A Case Series Study in Neonates and Infants.

2023· article· en· W4390956397 on OpenAlexaff
M K Renish, K. Krishnakumar, Ajit Kumar Pati, K S Sunoj, R. K. Menon

Bibliographic record

VenuePubMed · 2023
Typearticle
Languageen
FieldMedicine
TopicSurgical site infection prevention
Canadian institutionsASTER
Fundersnot available
KeywordsNegative-pressure wound therapyMedicineSurgeryWound dehiscenceSurgical debridementDehiscenceDebridement (dental)Wound healingMedian sternotomySurgical woundPlastic surgerySternumAnesthesia
DOInot available

Abstract

fetched live from OpenAlex

This case series, performed by the department of plastic and reconstructive surgery at our institution, reports the management of sternal wound dehiscence in newborns and children after cardiac surgery with the help of a negative pressure wound therapy treatment system. Three neonatal patients with poststernotomy wound problems were treated with a negative pressure wound therapy (VAC) system. Negative pressure therapy was started with negative pressure at 50 mm Hg, continuously. All children achieved healing of the sternal wound and a subsequent closure after a mean length of treatment of 33 days (range, 21-49 days). In conclusion, negative pressure therapy with pressure adjusted to lower values as compared with adults in combination with radical surgical debridement was found to be safe and effective, as well as being tolerated well in neonatal and infant patients with extensive or localized poststernotomy wound dehiscence.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.027
GPT teacher head0.294
Teacher spread0.267 · 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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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