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Record W2558734656 · doi:10.1097/jdn.0000000000000274

Nurses’ Experience Removing Superficial Nonabsorbable Sutures From the Skin

2016· article· en· W2558734656 on OpenAlexaff
Jennifer Akeroyd, Heather H. Kitada, Leslie Plauntz, William Lear

Bibliographic record

VenueJournal of the Dermatology Nurses’ Association · 2016
Typearticle
Languageen
FieldMedicine
TopicSurgical Sutures and Adhesives
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineWound dehiscenceFibrous jointDehiscenceSurgeryWound healing

Abstract

fetched live from OpenAlex

ABSTRACT In many dermatology settings, nurses remove superficial sutures from wounds of the skin. The current practice is to remove sutures between 5 and 14 days depending on the location of the defect to prevent wound overgrowth and subsequent complications. Yet, by 10 days, wound strength is only 5% of that of intact skin, also setting the stage for potential complications, such as dehiscence. We surveyed Dermatology Nurses’ Association members to understand their experiences with removing sutures from the skin. With a response rate of 16.75%, the sample size was 355 respondents. Over 90% of the respondents encountered wound overgrowth (i.e., ingrown sutures) when removing sutures. Moreover, mattress-type closures presented more difficulty on removal than simple-type closures. When sutures were ingrown, complications included patient pain (31.5%), wound bleeding (30.1%), inability to ensure the complete removal of sutures (19.2%), wound dehiscence (7.0%), and increased procedure time by nearly 2 minutes. Our findings of nurse experiences with suture removal support the need for innovative solutions to prevent wound overgrowth.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.008
GPT teacher head0.264
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

Citations4
Published2016
Admission routes1
Has abstractyes

Explore more

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