Bibliographic record
Abstract
We thank Irrgang for his thoughtful reflections on our article. His comments supported the fact that studies of prognosis are being recognized for their clinical importance and that—as we move toward the application of their results to clinical practice—their methods are undergoing the same scrutiny that randomized controlled trials received several years ago. There has been an exponential rise in the number of systematic reviews of prognosis over the past few years, and, with that, a rise in the need for standardization of the ways we conduct, report, and interpret studies of prognosis.2 Prognostic studies are vulnerable to biases in selection, prognostic factor assessment, study attrition, analytic approach, and outcome determination2 and require careful thought and description of the decisions made. We would like to respond to 3 of Irrgang's comments. First, the issues of format of the outcome; second, the issues of modeling; and finally, issues of moving forward from here—what was missed, and what lies ahead. In many ways, we do not dispute Irrgang's comments, and we appreciate the opportunity to respond to them.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".