Laser facial resurfacing: patient survey of recovery and results.
Bibliographic record
Abstract
OBJECTIVE: To determine patient satisfaction with the postoperative recovery period and surgical outcome after facial laser resurfacing. DESIGN: Survey. PATIENTS: Patients who underwent facial laser resurfacing by two of the authors (PAA or DAFE) between 1995 and 1996. Questionnaires were completed by 47 of 100 patients. INTERVENTIONS: Facial laser resurfacing with the Sharplan CO2 laser and SilkTouch Scanner. MAIN OUTCOME MEASURES: Questionnaire. Statistical analysis was generated from a 5-point analogue scale. RESULTS: The overall side effects were moderate in severity, were well tolerated, and met the expectations of our patients. Redness was the most frequent side effect with an average duration of 3 months. Most patients returned to occupational and social activities at 2.5 weeks. Moderate lifestyle disruption was more frequent after full-face laser resurfacing than partial-face laser resurfacing. Excellent improvement was achieved for removal of fine wrinkles, improvement of skin quality, and sun-damaged skin. Good improvement was obtained for acne scarring and deep rhytids. Overall goals were attained in 89% of patients. CONCLUSIONS: Laser resurfacing produced excellent results for fine rhytids, skin quality, and sun-damaged skin. Return to social and occupational activities may occur early in recovery with the use of camouflage make-up. Patient education and close follow-up are necessary in the postoperative period.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".