MétaCan
Menu
Back to cohort
Record W2024824060 · doi:10.1177/1538574411399157

The Role of Platelet-Rich Plasma in Inguinal Wound Healing in Vascular Surgery Patients

2011· article· en· W2024824060 on OpenAlex
Debbie A. Lawlor, Guy DeRose, Kenneth A. Harris, Marge Lovell, Teresa Novick, Thomas L. Forbes

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

VenueVascular and Endovascular Surgery · 2011
Typearticle
Languageen
FieldMedicine
TopicPeriodontal Regeneration and Treatments
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineSurgeryGroinPlatelet-rich plasmaWound healingIncidence (geometry)Randomized controlled trialDiabetes mellitusPlateletInternal medicine

Abstract

fetched live from OpenAlex

The objective was to determine whether incision application of platelet-rich plasma (PRP) will decrease postoperative wound complications in vascular surgery patients. A prospective, randomized trial randomized 81 incisions in 51 patients who underwent femoral artery exposure for elective revascularization procedures or endovascular abdominal aneurysm repairs. Incidence of diabetes, chronic renal failure, prosthetic grafts, body mass index (BMI), and steroid use did not differ. Using the ASEPSIS wound classification system, we found no difference in incidence of wound infection. Wound complications occurred in 9 (23%) of 40 of PRP group and 9 (22%) of 41 of non-PRP. Severe wound complications developed in 5 (13%) PRP and 6 (5%) of non-PRP (P = NS). In multivariate analysis, there were no predictors for wound infection. Groin wound complications rates are common in this patient group. Platelet-rich plasma did not decrease the incidence of groin wound complications in our 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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.221
Teacher spread0.200 · 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