MétaCan
Menu
Back to cohort
Record W188770145

Screening evaluation of an ionized nanocrystalline silver dressing in chronic wound care.

2001· article· en· W188770145 on OpenAlexaff
R. Gary Sibbald, A C Browne, Patricia Coutts, Douglas Queen

Bibliographic record

VenuePubMed · 2001
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineDebridement (dental)SurgeryExudateWound careChronic woundAntisepticWound healingPathology
DOInot available

Abstract

fetched live from OpenAlex

The successful topical treatment of chronic wounds requires adequate debridement, bacterial balance, and moisture balance. An ionized nanocrystalline silver dressing was evaluated through an uncontrolled, prospective study of a case series of 29 patients with a variety of chronic nonhealing wounds. The four arms of the study included nine patients with foot ulcers, six patients with venous stasis ulcers, two patients with pressure ulcers, and 12 patients with miscellaneous wounds. All wounds were assessed for the usual signs of clinical infection, with most of these parameters being measured and recorded. Microbiologically, bacterial load was determined via quantitative biopsies and semi-quantitative swabs. In general, the results showed a marked clinical improvement for the majority of wounds treated with the dressing. Among improved parameters included decreased exudate and decreased purulence. The quantitative bacterial biopsies did not show any decrease in organism numbers, although the semi-quantitative swabs indicated a decrease in the wound surface bacterial loading. This was indicative of the dressing's ability to reduce surface bacteria and achieve an element of bacterial balance in the superficial dermal compartment. The proposed mechanism of action for this ionized nanocrystalline based dressing is through bacterial and moisture balance within the superficial wound space compartment.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.085
GPT teacher head0.342
Teacher spread0.256 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations101
Published2001
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicWound Healing and TreatmentsFrench-language works237,207