Ten-Year Cephalometric Comparison of Patients With Cleft Palate who Received Treatment With Active or Passive Pre-surgical Orthopedic Devices
Bibliographic record
Abstract
Background Pre-surgical orthopedic (PSO) devices can be used in the management of patient with cleft lip/palate (CL/P) to narrow the alveolar gap (AG) prior to lip surgery. There are few studies comparing these 2 devices. The objective of this work was to compare the effects of active and passive PSO devices on facial growth in a single surgeon's cohort of patients with CL/P over a 10-year period. Methods A retrospective review of all patients with CL/P in a single surgeon's practice from 2002 to 2018 was performed. Preoperative measurements of AG size were done using electronic calipers on patient molds. Patient radiographs were taken at 5 and 10 years of age and cephalometric landmarks were plotted using specialized software. Independent sample t-tests were used to compare means for maxillary, mandibular, vertical, and dento-alveolar growth parameters. Results Twenty patients with an active device and 23 patients with a passive device were included. No differences were observed in the basic demographic information between the two groups. At the time of lip repair, patients with a passive device had significantly larger horizontal AGs ( P < .01), but by the time of palate repair, there was no difference between the two groups ( P = .94). There was no significant difference in any growth measurements between the active and passive device groups at 5 and 10 years. Conclusions Despite closing the AG more quickly, patients treated with an active device have no significant difference in facial growth at 10 years compared to patients treated with a passive device.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".