Case-Base Neural Network: survival analysis with time-varying, higher-order interactions
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
Note that this capsule does not reproduce the exact analysis found in the manuscript, as the computation time required is greater than 10 hours. The online version tests a fast hyperparameter set (only 1 set) and performs 2 fold bootstrap on the training set. The result it produces is not useful beyond making sure the code runs. If the user wants to reproduce the analysis, they must edit the code/run.sh script. Namely, set epo=2000 (default), iterations=100 (default) and quickGrid=0 (default). We recommend running the capsule with said modifications locally due to time restrictions on codeocean. feel free to contact us if you have any issues. Case-Base Neural Networks (CBNNs) estimate the full hazard function. It naturally accounts for censoring and predicts smooth-in-time risk functions. Uses a simple objective function and models time-varying effects by design, unlike competing methods. CBNNs outperform the competing models in a simulation and two studies, with competitive performance in a third study.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.009 | 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 it