Reporting Standards and Critical Appraisal of Prediction Models
Bibliographic record
Abstract
Abstract Prediction models have the potential to positively influence clinical decision-making and thus the overall quality of healthcare. The translational gap needs to be bridged between development of complex statistical models requiring multiple predictors and widespread usage in clinical consultation. A recent review found that inadequate quality of reporting of prediction modelling studies could be a contributing factor in slow transition to the clinic. This chapter emphasises the importance of high-quality reporting of modelling studies and the need for critical appraisal to understand the potential issues limiting generalizability of published models. Evidence synthesis (such as systematic reviews and pooled analysis of disparate models) are relatively under-represented in literature, though methodological studies and guidelines are now starting to appear.
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 | 0.675 | 0.883 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.037 | 0.025 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.025 | 0.012 |
| Open science | 0.017 | 0.013 |
| Research integrity | 0.019 | 0.023 |
| Insufficient payload (model declined to judge) | 0.016 | 0.012 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".