Bibliographic record
Abstract
PURPOSE OF REVIEW: Cleft lip repair requires multidisciplinary follow-up throughout a child's life and often requires lip revision surgery in adolescence to restore function and symmetry of the lip. There is significant variability in the approaches taken for lip repair and therefore a review of current techniques and subsequent guidance to secondary cleft lip repair is warranted. RECENT FINDINGS: New methods of secondary reconstruction can be divided into superficial or muscle related. Recent suggestions for superficial reconstruction include botulinum toxin injection, silicone gel sheeting, local flap reconstruction, fat grafting, and CO2 laser ablation. Suggestions for muscular reconstruction include pedicled prolabial flaps, modified Abbe flap, and orbicularis oris eversion. SUMMARY: Secondary cleft lip deformities can be classified as superficial or muscle related. Superficial problems require relatively minor treatments such as laser, local scar revisions, small local flaps, mucosal excision, or fat grafting. Muscle deformities generally require total lip revision and rerepair as a first step to achieving longstanding improvements in lip esthetics and function. Cleft lip revision should only be considered in concert with the patient, be based on the patient's concerns and desires, and offered at the appropriate timeline to improve social integration and/or psychosocial wellbeing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".