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Record W4318755156 · doi:10.12968/jowc.2023.32.sup2.s3

What is best practice in resource-limited settings?

2023· article· en· W4318755156 on OpenAlexaff
David Keast

Bibliographic record

VenueJournal of Wound Care · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsParkwood Institute
Fundersnot available
KeywordsBest practiceMedicineResource (disambiguation)Health careClinical PracticeMEDLINEAllianceLibrary scienceBest evidenceMedical educationFamily medicineManagementComputer scienceHistory

Abstract

fetched live from OpenAlex

Advanced regenerative therapies using cellular and tissue-based products (CTPs) can play an important role in effective management of hard-to-heal wounds. CTPs derived from allogenic or xenogenic tissues use an extracellular matrix (ECM) to provide a therapeutic ECM scaffold in the wound bed to facilitate tissue regeneration. One such example is OASIS Extracellular Matrix (Cook Biotech Incorporated), a porcine small intestinal submucosa extracellular matrix (SIS-ECM) that preclinical and clinical data have shown to be tolerable and effective in promoting tissue regeneration in hard-to-heal 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.050
metaresearch head score (Gemma)0.224
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.050
Threshold uncertainty score0.264

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.224
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.004
Science and technology studies0.0030.006
Scholarly communication0.0160.013
Open science0.0040.007
Research integrity0.0100.009
Insufficient payload (model declined to judge)0.0280.006

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 head0.219
GPT teacher head0.438
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2023
Admission routes1
Has abstractyes

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