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Wounds in Surgical Patients Who Are Obese

2007· review· en· W126287595 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAJN American Journal of Nursing · 2007
Typereview
Languageen
FieldMedicine
TopicBody Contouring and Surgery
Canadian institutionsContinental (Canada)
Fundersnot available
KeywordsMedicinePerioperativeIntensive care medicineDehiscencePopulationGeneral surgeryObesitySurgeryInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

In Brief Surgery, whether bariatric or not, puts this population at risk. Review the basics of prevention and care. Overview The number of surgical patients who are obese in the United States is rising, a trend that's likely to continue. Such patients are at higher risk than nonobese patients are for surgical site infections and other complications such as dehiscence, pressure ulcers, deep tissue injury, and rhabdomyolysis. This article details the factors that can contribute to such complications, including a high number of comorbidities, and offers practical suggestions for preventing them. Nurses should understand that special equipment, precautions, and protocols may be needed at every stage of care, and that obese patients aren't anomalies but rather a part of a growing population with particular needs. Obesity increases the risk of perioperative complications in the skin and underlying tissue, including infection, dehiscence, pressure ulcers, and deep-tissue injury. Vigilant monitoring can be lifesaving.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.073
GPT teacher head0.411
Teacher spread0.338 · 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