Case-Base Neural Network: survival analysis with time-varying, higher-order interactions
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.
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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.065 | 0.010 |
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".