Nasal Deformity Correction in Cleft Lip and Palate Patients by Secondary Rhinoplasty: Patients Satisfactions
Bibliographic record
Abstract
AIM OF THE STUDY: To evaluate the degree of patient satisfaction postsecondary rhinoplasty, specifically in cleft patients in Mosul city. METHODOLOGY: A retrospective comparison questionnaire study was conducted between March and April 2024 among cleft patients who underwent surgical secondary rhinoplasty in Mosul City of Nineveh Province. All cleft patients aged 18 to 40 who underwent secondary rhinoplasty are included in the study. Forty participants respond to a specific questionnaire in 3 time periods (before surgery 3 and 6 months after). Basic demographic information (age, gender, cause of visit, diagnosis, and treatment) is recorded. Questionnaire which conflict the patient's perception presurgery and postsurgery also. The improvement rate and reduction rate of improvement are calculated according to a special equation.Thanks for such comment. The sentences changed as requested with (Six standard questions applied to evaluate the patients' satisfaction postsurgery. Responses are assessed statistically by special equation to detect the improvement rate). RESULTS: According to age groups, the age between 26 and 30 years have the highest incidence between the cases with 37.5%. Females are highest with (55%). Esthetics are the most common cause. Effective or improvement rate shows positive except for question 6 ( median: 4%). The reduction rate shows positive results. CONCLUSIONS: Despite being a complicated surgical operation, cleft rhinoplasty can yield symmetry, improved esthetics, and enhanced functioning, including improved breathing, which can be life-changing for patients and fulfilling for the surgeon. Its difficulty stems from the fact that it has required several surgical phases throughout the years, each of which calls for a comprehensive surgical team with a variety of specializations to be addressed for optimal outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".