A paired comparison analysis of third-party rater thyroidectomy scar preference
Bibliographic record
Abstract
OBJECTIVE: To determine the length and position of a thyroidectomy scar that is cosmetically most appealing to naïve raters. METHODS: Images of thyroidectomy scars were reproduced on male and female necks using digital imaging software. Surgical variables studied were scar position and length. Fifteen raters were presented with 56 scar pairings and asked to identify which was preferred cosmetically. Twenty duplicate pairings were included to assess rater reliability. Analysis of variance was used to determine preference. RESULTS: Raters preferred low, short scars, followed by high, short scars, with long scars in either position being less desirable (p < 0.05). Twelve of 15 raters had acceptable intra-rater and inter-rater reliability. CONCLUSION: Naïve raters preferred low, short scars over the alternatives. High, short scars were the next most favourably rated. If other factors influencing incision choice are considered equal, surgeons should consider these preferences in scar position and length when planning their thyroidectomy approach.
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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 | 0.021 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".