Du nouveau dans l’absorption intestinale du cholestérol : NPC1-L1
Bibliographic record
Abstract
636 des produits de sante. Paris: AFSSAPS, 2001. 2. Evans WE, Relling MV. Pharmacogenomics : translating functional genomics into rational therapeutics. Science 1999 ; 286 : 487-91. 3. Oscarson M. Pharmacogenetics of drug metabolising enzymes : importance for personalised medecine. Clin Chem Lab Med 2003; 41 : 573-80. 4. Zanger UM, Raimundo S, Eichelbaum M. Cytochrome P450 2D6: overview and update on pharmacology, genetics, biochemistry. Naunyn-Schmiedeberg’s Arch Pharmacol 2004; 369 : 23-37. 5. Kirchheiner J, Brosen K, Dahl ML, et al. CYP2D6 and CYP2C19 genotype-based dose recommendations for anti-depressants : a first step towards subpopulation-specific dosages. Acta Psychiatr Scand 2001; 104 : 173-92. 6. Relling MV, Hancock ML, River S, et al. Mercaptopurine therapy intolerance and heterozygoty at the thiopurine S-methyl-transferase gene locus. J Natl Cancer Inst 1999; 91 : 2001-8. 7. Ando Y, Saka H, Ando M, et al. Polymorphisms of UDPglucuronosyl-transferase gene and irinotecan toxicity : a pharmacogenetics analysis. Cancer Res 2000, 60 : 6921-6. 8. Daly AK, King BP. Pharmacogenetics of oral anticoagulants. Pharmacogenetics 2003; 13 : 247-52. 9. Anglicheau D, Verstuyft C, Laurent-Puig P, et al. Association of the multidrug resistance-1 gene single-nucleotide polymorphisms with the tacrolimus dose requirements in renal transplant recipients. J Am Soc Nephrol 2003, 14 : 1889-96. 10. Swiss HIV Cohort Study. Response to antiretroviral treatment in HIV-1-infected individuals with allelic variants of the multidrug resistance transporter 1 : a pharmacogenetics study. Lancet 2002; 359 : 30-6.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".