Robin Sequence: Clearing Nosologic Confusion
Bibliographic record
Abstract
OBJECTIVE: To gather evidence surrounding the confusion in the classification of Robin sequence and inform those who have the power to make the changes in defining this symptom complex. METHOD: A questionnaire was sent to all participating cleft palate teams (N=204) of the American Cleft Palate-Craniofacial Association. The questionnaire identified the precise, different characteristics for diagnosing Robin sequence and evaluated whether the difference between a retrognathia and micrognathia influenced the diagnosis process. We subsequently also investigated whether the cleft type (i.e., U-shaped versus V-shaped) had any influence in the decision-making process. A PubMed literature review of the 50 most recent manuscripts about Robin sequence was evaluated also. RESULTS: Seventy-three questionnaires were received. This 35% response rate revealed 14 different definitions of Robin sequence. A PubMed literature review of 50 consecutive manuscripts revealed 15 different descriptions. CONCLUSION: This study confirms that nosologic confusion is widespread with regard to defining Robin sequence. This has implications for evaluating Robin sequence, giving advice about the prognosis and genetic counseling, and refining treatment options.
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 | 0.027 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".