Comparison of three-dimensional and two-dimensional templates on auricle reconstruction in patients with unilateral microtia.
Bibliographic record
Abstract
To confirm the advantage of 3D template over the traditional 2D template in auricle reconstruction. Two hundred patients with Marx III unilateral microtia treated in our hospital during the last four years were included in this retrospective study. They were divided into two groups according to the surgery which was assisted by 2D or 3D template. The outcome was evaluated 6 months after the surgery in the following aspects: the mean surgical time, the similarity rate for ear size, nasal-tip to tragus length and auriculocephalic angle, the patient's satisfaction and the quality of life after surgery. The surgical time for the 3D group was 3.2 ± 1.9 hours, significantly shorter than that for the 2D group (4.1 ± 3.7 hours; P < 0.05). The similarity rates between both sides were 91.24 ± 1.71%, 96.46 ± 2.51%, and 88.15 ± 10.20% respectively for ear size, nasal tip-tragus length, and auriculocephalic angle in the 3D group. While the corresponding values in the 2D group were smaller and were 87.47 ± 3.66%, 90.16 ± 3.27%, and 78.25 ± 1.26% respectively. The difference was significant in nasal tip-tragus length and auriculocephalic angle (P < 0.05), but not for ear size (P > 0.05). The patients' satisfaction was better in the 3D group. The averaged GCBI score was 65.6 ± 13.2 in the 3D group, which was significantly higher than the value of 55.3 ± 16.8 in the 2D group (P < 0.05). The use of 3D template resulted in a better outcome in the auricle reconstruction 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".