Analysis of trends and status of evaluation methods in thyroid scar
Bibliographic record
Abstract
Background: The incidence of thyroid cancer has increased over the decades, and patients prefer short thin scars after thyroidectomy due to their cosmetic visibility. Several scar assessment methods have been used to determine the most cosmetically optimal surgical method, but a widely accepted measurement tool is still lacking. This study investigates the usage status in the thyroid scar scale according to time, region, and study method. Methods: The authors searched for articles on thyroid scars published between January 2000 and September 2022 in the PubMed database. The study included clinical studies that mentioned thyroid scar and scar scale, excluding articles that did not evaluate neck scars. Statistical analysis was performed using IBM SPSS Statistics 29. Results: A total of 35 studies were included. Among them, 17 used the Vancouver Scar Scale (VSS), 17 used the Patient and Observer Scar Assessment Scale (POSAS), four used the Manchester Scar Scale (MSS), and four used the Stony Brook Scar Evaluation Scale (SBSES). VSS and POSAS were the most commonly used scar evaluation methods. VSS tended to be used frequently in Asia, while POSAS was used frequently in Europe and in randomized controlled trials. Conclusion: VSS and POSAS are popular thyroid scar assessment methods, with regional variations. Standardization is needed for meaningful comparisons. Patient's subjective evaluations should be considered, given the cosmetic importance of thyroid scars.
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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 | 0.026 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.011 | 0.018 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".