A quarter of a a century in craniomaxillofacial distraction osteogenesis. Where are we now; Where are we going?
Bibliographic record
Abstract
Distraction osteogenesis has been one of the most innovative \nconcepts in craniomaxillofacial surgery through the last 25 \nyears. \nIn 1987 Cesar A. Guerrero first performed a mandibular \nwidening by distraction osteogenesis. Joseph G. McCarthy in \n1992 published an extensive paper on the treatment of hemifacial \nmicrosomia. \nIn 1994 Wangerin and Gropp and in 1996 Diner et al. \npublished on the use of intraoral devices for mandibular \ndistraction. After a quarter of century of extensive use Distraction \nOsteogenesis has today specific indications for congenital \ncraniofacial and cleft deformities. \nTechnology has evolved from the first application of external \ndevices to intraoral and hybrid or semiburied techniques. \nIn congenital craniomaxillofacial anomalies distraction is \nindicated during growth. \nMandibular distraction osteogenesis can be safely and effectively \nused to avoid or remove tracheostomy in neonates with \nsevere airway obstruction caused by micrognatia in Pierre \nRobin sequence. \nThere is a great range of indications of DO in pediatric craniofacial \ndeformities and this new concept seems to well \ncombine the proven Tessier principle of “ first build, then \nmove ”. \nOn the other hand thorough Team evaluation needs to be \nesthablished prior to the surgical decision, and the indications \nof early distraction in neonates should be well-considered.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".