Assessing cosmetic results after conventional thyroidectomy using the EASY-EYE_C: a double-blind randomized controlled trial
Bibliographic record
Abstract
PURPOSE: The incidence of thyroid cancer is relatively high, especially in young women, and postoperative scarring after thyroidectomy is an important problem for both patients and clinicians. Currently, there is no available product that can be used for wound protection during thyroid surgery. We used the EASY-EYE_C, a new silicone-based wound protector. METHODS: We conducted a double-blind randomized controlled trial to assess the efficacy of the EASY-EYE_C with surgical scars. We studied 66 patients who underwent conventional total thyroidectomy or hemithyroidectomy performed by a single surgeon from August 2015 to June 2016. At 6-week follow-up, a single blinded physician observed the wounds to make clinical assessments using the Patient and Observer Scar Assessment Scale (POSAS), the Vancouver Scar Scale (VSS), and a modified Stony Brook Scar Evaluation Scale (SBSES). RESULTS: There were no significant differences by sex, age, type of surgery, body mass index, length of wound, incision site (from sternal notch), or thyroid weight, but the duration of operation was significantly shorter in the experimental group (E group). The e-group also had better POSAS scores than the control group (C group), with means of 43.2 (standard deviation [SD], ±15.9) versus 68.3 (SD, ±21.5), respectively (P < 0.05). The modified SBSES and VSS scores were similar to those from the POSAS. CONCLUSION: In this study, all scores for evaluating outcomes were higher in the E group than in the C group. In addition, the operation time was significantly shorter in the E group. Therefore, the EASY-EYE_C may be useful for improving the cosmetic outcomes of conventional thyroid surgery.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".