Bibliographic record
Abstract
Stratified approaches to treating disease are very attractive, as efficacy is maximised by identifying responders using a companion diagnostic or by careful phenotyping. This approach will spare non-responders form potential side-effects. This has been pioneered in oncology where single genes or gene signatures indicate tumours that will respond to specific chemotherapies. Stratified approaches to the treatment of asthma with biological therapies are currently being extensively studied. In cystic fibrosis (CF), therapies have been developed that are targeted at specific functional classes of mutations. Ivacaftor, the first of such therapies, potentiates dysfunctional cystic fibrosis transmembrane conductance regulator (CFTR) protein Class III mutations and is now available in the USA and some European countries. Pivotal studies in patients with a G551D mutation, the most common Class III mutation, have demonstrated significant improvements in clinically important outcomes such as spirometry and exacerbations. Sweat chloride was significantly reduced demonstrating a functional effect on the dysfunctional CFTR protein produced by the G551D mutation. Symptom scores are also greatly improved to a level that indicates that this is a transformational treatment for many patients. This stratified approach to the development of therapies based on the functional class of the mutations in CF is likely to lead to new drugs or combinations that will correct the basic defect in many patients with CF.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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; both teacher heads agree on what is shown here.
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".