Evaluation of Research Diagnostic Criteria in Craniofacial Microsomia
Bibliographic record
Abstract
Characteristics of patients with craniofacial microsomia (CFM) vary in type and severity. The diagnosis is based on phenotypical assessment and no consensus on standardized clinical diagnostic criteria is available. The use of diagnostic criteria could improve research and communication among patients and healthcare professionals. Two sets of phenotypic criteria for research were independently developed and based on multidisciplinary consensus: the FACIAL and ICHOM criteria. This study aimed to assess the sensitivity of both criteria with an existing global multicenter database of patients with CFM and study the characteristics of patients that do not meet the criteria. A total of 730 patients with CFM from were included. Characteristics of the patients were extracted, and severity was graded using the O.M.E.N.S. and Pruzansky-Kaban classification. The sensitivity of the FACIAL and ICHOM was respectively 99.6% and 94.4%. The Cohen's kappa of 0.38 indicated a fair agreement between both criteria. Patients that did not fulfill the FACIAL criteria had facial asymmetry without additional features. It can be concluded that the FACIAL and ICHOM criteria are accurate criteria to describe patients with CFM. Both criteria could be useful for future studies on CFM to create comparable and reproducible outcomes.
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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".