Bibliographic record
Abstract
PURPOSE OF REVIEW: Although significant strides have been made in genome sequencing technology, target-drug matching remains challenging. This article highlights the difficulties associated with patients accessing targeted drugs based on genomic information, and some proposed solutions. RECENT FINDINGS: Although cancers are increasingly stratified according to molecular subgroups, challenges remain in improving patient outcome based on drug-target matching. Before a drug-target match is even proposed, significant expertise is required of the clinician to interpret genomic information. Once a potential match is made, barriers remain for patients to access treatment via clinical trials, as approved agents on-label or off-label, or through expanded access programs. Solutions to improve drug accessibility are actively being investigated. Several prospective trials using molecular characterization as an entry to access target-drug matching are underway. For those unable to access target-drug matching on trial, proposals for a facilitated access program and registry have been suggested. SUMMARY: Although improvements have been made in the drug development and approval timelines, drug accessibility based on molecular characterization remains problematic. However, with the emergence of novel trial designs, and efforts to enhance drug access outside of clinical trial settings, opportunities for drug-target matching are improving.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.017 |
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".