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 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 | 0.012 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.032 | 0.034 |
| Insufficient payload (model declined to judge) | 0.056 | 0.026 |
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".