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Vitamin and Trace Element Loss from Negative-Pressure Wound Therapy

2015· article· en· W2397423123 on OpenAlexaff
Leslie Hourigan, Stanley T. Omaye, Carl L. Keen, John A. Jones, Michael A. Dubick

Bibliographic record

VenueAdvances in Skin & Wound Care · 2015
Typearticle
Languageen
FieldMedicine
TopicSurgical site infection prevention
Canadian institutionsCegep de Sept Iles
Fundersnot available
KeywordsMedicineNegative-pressure wound therapyMicronutrientAbdomenExudateVitaminSurgerySoft tissueWound healingVitamin CInternal medicinePathology

Abstract

fetched live from OpenAlex

OBJECTIVE: This study investigated select vitamin and trace element loss from wound exudates in burn and trauma patients treated with negative-pressure wound therapy (NPWT). DESIGN: A prospective observational study was performed using wound exudate samples. SETTING: A level I trauma center acute care hospital. PARTICIPANTS: The study was composed of 8 patients with open abdomens and 9 patients with 12 soft-tissue wounds. MAIN OUTCOME MEASURES: The goal was to collect wound exudate samples daily for 3 days, then every other day to day 9 or until NPWT was discontinued, and to analyze for vitamins A (retinol), C, and E and zinc (Zn), iron (Fe), and copper (Cu). Daily loss of each micronutrient was calculated from their concentration and 24-hour volumes of the exudates. MAIN RESULTS: Exudate loss in the open-abdomen group was significantly higher than in the patients with soft-tissue wounds (900 ± 547 vs 359 ± 246 mL/d). The mean 24-hour loss of vitamins A, C, and E were 0.3, 2.8, and 11 mg, respectively, in the open-abdomen group. Over the same period, the losses of Zn, Fe, and Cu were 0.5, 0.4, and 0.25 mg, respectively, in these patients. Micronutrient 24-hour loss was significantly lower in the soft-tissue wound patients than in the open-abdomen group. CONCLUSIONS: The data support the concept that significant amounts of micronutrients can be lost from NPWT wound exudates, particularly in open abdomens. These losses should be considered in the nutritional support of these patients who typically are in a hypermetabolic and catabolic state.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.310
Teacher spread0.296 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations9
Published2015
Admission routes1
Has abstractyes

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