Bibliographic record
Abstract
Ce syndrome est une maladie autosomique recessive caracterisee par une obesite, une retinite pigmentaire, une polydactylie, des difficultes d'apprentissage, un hypogonadisme et des malformations renales. Les autres caracteristiques sont un diabete, une dysfonction endocrinienne complexe et des troubles du comportement. Le tableau clinique est relativement uniforme, mais 5 loci genetiques ont ete rapportes.\rEn utilisant un criblage du genome a partir de familles originaires de Terre-Neuve, une liaison a ete identifiee en 20p12. Une cartographie fine a permis de reduire les recherches a un intervalle dans lequel est situe un gene codant pour une molecule chaperonne, dont les mutations sont responsables d'un syndrome de McKusick-Kaufman. Compte tenu du recoupement phenotypique des deux maladies, le gene MKKS a ete etudie et des mutations identifiees dans 5 familles canadiennes et 2 familles americano-europeenne porteuses de syndrome de Bardet-Biedl [1]. Des resultats identiques sont rapportes par une equipe americaine a propos de 3 familles de Bardet-Biedl [2]. Le produit du gene MKKS a des similarites avec les chaperonines de type 2 responsables du repliement de toute une serie de proteines.1. Ktsanis N. et al. 2000. Mutations in MKKS cause obesity, retinol dystrophy and renal malformations associated with Bardet-Biedl syndrome. Nat Gen 26 : 67-70.2. Slavotinek A.M. et al. 2000. Mutations in MKKS cause Bardet-Biedl syndrome. Nat Gen 26 : 15-16.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".