A Descriptive Examination of the Impact of Sternal Scar Formation in Women
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".