Bibliographic record
Abstract
The paper by Tangri et al. provides a clear overview of the studies he performed regarding the development and validation of the kidney failure risk equation (KFRE). This prediction model has been shown to have a good discriminatory ability for patients with future renal failure as well as a good calibration, also when it was tested in several other study populations. This is quite exceptional, as most published risk scores have a much lower predictive value in external validation studies, if they are even externally validated. Even more important, using the KFRE has been shown to have an impressive impact on relevant outcomes in clinical practice, as after implementation in Manitoba, Canada, new referrals dropped by 35% and waiting times for nephrology consultation declined by 90%. This convincingly proves that at least not all clinical risk scores are useless. Positive examples as the KFRE are desperately needed, as most systematic reviews and educational papers clearly show that the vast majority of prediction models have quite poor predictive value. To quote Dr Tangri: ‘Nevertheless, clinical predictive models follow the familiar (now predictable) yet depressing pattern of translation: many are made, few are validated, and almost none are used’ [1].
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 | 0.009 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.024 | 0.028 |
| Insufficient payload (model declined to judge) | 0.074 | 0.036 |
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".