Bibliographic record
Abstract
In Reply Dr Karp raises concerns about the way we explained several statistical concepts to a wide audience of practicing clinicians.With respect to the author's first 5 points, he is technically correct.We would argue, however, that our presentation is very close to accurate technically, understandable to clinicians, and will not result in misleading inferences.Experience with other Users' Guides in which we have used similar approaches-ie, pragmatic explanations that capture the essence of the concept but that may not be technically pristine-gives us considerable confidence in this inference.1 With respect to the author's final point, although we agree that any guideline panel ideally includes a methodologist (although not necessarily a statistician), clinical decisions also require clinical insight and expertise.Clinicians with the right training (eg, exposure to the relevant Users' Guides) can grasp the essence of the message emerging from statistical presentations of evidence-such as the various types of adjusted analysis to deal with prognostic imbalance-and incorporate their understanding of results into astute judgments regarding appropriate management of patient care.
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 | 0.011 | 0.217 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.124 | 0.106 |
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".