Bibliographic record
Abstract
Machine learning (ML) is a powerful and flexible tool that can be used to analyze and predict outcomes from biological and clinical data. ML models have the potential to improve healthcare efficiency in a number of ways. Algorithms that predict prognosis empower healthcare officials to allocate resources optimally and physicians to select better treatment options for patients. Diagnostic models can be used in screening, in risk stratification, and to recommend appropriate testing and treatment. This would decrease the burden on physicians, increase and expedite patient access to care, save resources, and reduce costs. However, despite the research advances of ML in medicine, its role in the clinic is currently limited. Model building and validation may require large amounts of high-quality data that can be difficult and expensive to obtain, and diagnostic models must be individually built for each disease, a lengthy process. The psychological aspect of trusting black box algorithms may also be challenging to accept. Continued ML research, however, may enable the use of smaller datasets and the development of more transparent models. Careful trials in the clinic will need to be conducted before the more impactful uses of ML, such as diagnosis, can be implemented.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 teacher head, 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".