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Record W4376869874 · doi:10.1126/sciadv.ade7007

Breakthrough treatments for accelerated wound healing

2023· review· en· W4376869874 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

VenueScience Advances · 2023
Typereview
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsUniversité de Montréal
FundersNational Institute on Aging
KeywordsWound healingMedicineIntensive care medicineClinical trialBroad spectrumBioinformaticsSurgeryPathologyBiology

Abstract

fetched live from OpenAlex

Skin injuries across the body continue to disrupt everyday life for millions of patients and result in prolonged hospital stays, infection, and death. Advances in wound healing devices have improved clinical practice but have mainly focused on treating macroscale healing versus underlying microscale pathophysiology. Consensus is lacking on optimal treatment strategies using a spectrum of wound healing products, which has motivated the design of new therapies. We summarize advances in the development of novel drug, biologic products, and biomaterial therapies for wound healing for marketed therapies and those in clinical trials. We also share perspectives for successful and accelerated translation of novel integrated therapies for wound healing.

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)0.000
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 score0.993
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
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 head0.242
GPT teacher head0.506
Teacher spread0.265 · 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