Determination of Novel, Cranium-Based Relationships for Construct Placement in Microtia Reconstruction for Hemifacial Microsomia Patients
Bibliographic record
Abstract
Objective Determine if the ideal location of the construct in microtia reconstruction for hemifacial microsomia (HFM) can be more accurately derived from measurements on the cranium. Design High-resolution computerized tomography (CT) images were analyzed through craniometric linear relationships. Setting Our tertiary care institution from 2000 to 2021. Patients/Participants Patients diagnosed with HFM and microtia, who had high-resolution craniofacial CT scans, yielding 36 patients accounting for 44 CT scans. Main Outcome Measure(s) First, the integrity of the posterior cranial vault among HFM patients was determined. If proven to be unaffected, it could be used as a reference in the placement of the construct. Second, the position of the ear in relation to the cranium was assessed in healthy age-matched controls. Third, if proven to be useful, the concordance of these cranium-based relationships could be validated among our HFM cohort. Results The posterior cranial vault is unaffected in HFM ( P > .001). Further, craniometric relationships between the tragus and the Foramen Magnum, as well as between the tragus and the posterior cranium, have been shown to be highly similar and equally precise in predicting tragus position in healthy controls ( P > .001). These relationships held true across all age groups ( P > .001), and importantly among HFM patients, where the mean absolute difference in predicted tragus position never surpassed 1.5 mm. Conclusions Relationships between the tragus and the cranium may be used as an alternative to distorted facial anatomy or surgeon's experience to assist in pre-operative planning of construct placement in microtia reconstruction for HFM patients.
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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 | 0.003 |
| 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) | 0.002 | 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".