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A Descriptive Examination of the Impact of Sternal Scar Formation in Women

2008· article· en· W2106387725 on OpenAlexafffund
Kathryn King, Judith McFetridge-Durdle, Pamela LeBlanc, Alexander Anzarut, Ross T. Tsuyuki

Bibliographic record

VenueEuropean Journal of Cardiovascular Nursing · 2008
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsDalhousie UniversityUniversity of AlbertaUniversity of Calgary
FundersCanadian Institutes of Health ResearchFondation pour la Recherche MédicaleInstitute of Gender and HealthUniversity of AlbertaHeart and Stroke Foundation of Canada
KeywordsMedicineDescriptive statisticsDescriptive researchPhysical examinationSurgery

Abstract

fetched live from OpenAlex

Formation of abnormal scars is a significant source of morbidity following sternotomy. We undertook a descriptive exploratory mixed methods study of women (n=13) who participated in the Women's Recovery from Sternotomy Trial to examine the: (1) qualitative impact of the cosmetic result of sternotomy, and (2) quantitative association between subjective satisfaction and objective ratings of the sternal scar. Conventional content analysis was used to analyze the data generated from semi-structured interviews. Though the participants appreciated that having the scar was a cost of reaping the benefits of having cardiac surgery, they were not well prepared to learn to live with the scar. The scar was a poignant personal reminder that they had a health problem and underwent a distressing surgery, and it often rendered them feeling less attractive. The scar also had a public presence that they perceived rendered judgment from others. There was little association between the participants' subjective satisfaction (rated on a likert-type scale) and the objective scar rating using the Beausang Clinical Scar Assessment (r=0.348, p=0.294). The subjective perception of the sternal scar is of importance to women. Thus, appropriate preparation, post-operative counseling and support regarding the sternal scar are warranted.

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)0.002
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 score0.845
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.023
GPT teacher head0.245
Teacher spread0.222 · 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

Citations22
Published2008
Admission routes2
Has abstractyes

Explore more

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