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Record W2916745449

Ten top tips...Preventing orthopaedic surgery-related wound blisters

2014· article· de· W2916745449 on OpenAlexaff
Warren Gillibrand

Bibliographic record

VenueHuddersfield Research Portal (University of Huddersfield) · 2014
Typearticle
Languagede
FieldMedicine
TopicSurgical Sutures and Adhesives
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsBlistersMedicineSurgeryOrthopedic surgeryWound closureWound healing
DOInot available

Abstract

fetched live from OpenAlex

Superficial wound blisters are an abnormal swelling (i.e. filling with fluid) in the epidermal layer of the skin in response to trauma. Blistering in postoperative wounds may be caused by skin stripping from removal of medical tape, or prolonged exposure of the skin to adhesive contact layers of dressings and associated with the presence of sutures. Deeper dermal blisters are generally associated with burns or direct trauma and can take longer to heal than superficial blisters. Postsurgical blistering can cause pain, wound leakage, delay healing of the wound, and increase the risk of postoperative surgical site infection, which ultimately can result in prolonged and costly hospital stays.

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)0.001
metaresearch head score (Gemma)0.005
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: Other · Consensus signal: Other
Teacher disagreement score0.104
Threshold uncertainty score0.347

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1040.031

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 head0.050
GPT teacher head0.296
Teacher spread0.246 · 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
GenreOther

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
Published2014
Admission routes1
Has abstractyes

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