MétaCan
Menu
Back to cohort
Record W3184606829 · doi:10.1055/s-0041-1731460

Updates in Diabetic Wound Healing, Inflammation, and Scarring

2021· review· en· W3184606829 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

VenueSeminars in Plastic Surgery · 2021
Typereview
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineWound healingDiabetes mellitusInflammationAngiogenesisPathologicalGlycemicSurgeryWound dehiscenceDiseaseDehiscenceInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Diabetic patients can sustain wounds either as a sequelae of their disease process or postoperatively. Wound healing is a complex process that proceeds through phases of inflammation, proliferation, and remodeling. Diabetes results in several pathological changes that impair almost all of these healing processes. Diabetic wounds are often characterized by excessive inflammation and reduced angiogenesis. Due to these changes, diabetic patients are at a higher risk for postoperative wound healing complications. There is significant evidence in the literature that diabetic patients are at a higher risk for increased wound infections, wound dehiscence, and pathological scarring. Factors such as nutritional status and glycemic control also significantly influence diabetic wound outcomes. There are a variety of treatments available for addressing diabetic wounds.

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)0.002
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 score0.876
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
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 head0.039
GPT teacher head0.326
Teacher spread0.287 · 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