Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To curb the high cost of drug development, there is an urgent need to develop more predictive tissue models using human cells to determine drug efficacy and safety in advance of clinical testing. Recent insights gained through fundamental biological studies have validated the importance of dynamic cell environments and cellular communication to the expression of high fidelity organ function. Building on this knowledge, emerging organ-on-a-chip technology is poised to fill the gaps in drug screening by offering predictive human tissue models with methods of sophisticated tissue assembly. Organ-on-a-chip start-ups have begun to spawn from academic research to fill this commercial space and are attracting investment to transform the drug discovery industry. This review traces the history, examines the scientific foundation and envisages the prospect of these renowned organ-on-a-chip technologies. It serves as a guide for new members of this dynamic field to navigate the existing scientific and market space.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.006 |
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 it