Steel scalpel versus electrocautery blade: comparison of cosmetic and patient satisfaction outcomes of different incision methods.
Bibliographic record
Abstract
OBJECTIVE: To determine which method of skin incision has superior cosmetic and patient satisfaction outcomes. METHODS: Consenting patients undergoing bilateral neck dissection who met the inclusion criteria were prospectively enrolled. Each side of the neck was randomly assigned into one of the following two groups: scalpel incision and electrocautery incision. Cosmetic and patient satisfaction outcomes were collected prospectively with patients and outcome assessors blinded to group assignment. Validated self-report questionnaires and objective scar measures were used. RESULTS: Nineteen patients met the criteria for inclusion. Analysis revealed no significant differences between groups in terms of cosmetic or satisfaction outcomes. Use of the steel scalpel was found to result in significantly greater incision-related blood loss compared with use of the electrocautery blade. CONCLUSION: Steel scalpel or electrocautery may be used to incise the skin of patients undergoing bilateral neck dissection with no difference in cosmetic or patient satisfaction outcome. The steel scalpel yields greater incision-related blood loss compared with the electrocautery blade.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".