Risk prediction models: are they really necessary?
Bibliographic record
Abstract
. Initially created with the aim of analyzing the likelihood of complications and deaths in patients undergoing intervention, these prediction models allow to evaluate the risks and benefits of the procedure. Although no prediction system is comprehensive enough to estimate the specific outcome for each patient, risk stratification allows patients and physicians to know the likely risk of complications or death for the group of individuals with similar risk profile undergoing the proposed procedure collaborating in making decisions. Moreover, these multivariate models of risk assessment have been applied in comparing the performance of institutions or individual professionals, such as setting an objective way to measure the quality of health services, and assisting in the adjustment of resource allocation. Although they are subject to much criticism, the risk assessment models are obviously superior to the comparison of absolute numbers, such as mortality rates, in evaluating the performance of groups or hospitals. Most of the prediction systems developed in cardiac surgery were developed from large populations of patients, resulting often in multicenter studies. From these data, risk scores are established, based on factors identified as predictors of death or complications. The fact is that since the first risk score has become widely known - the Parsonnet index, in the 80s of last century - a wide variety of these instruments has been proposed, including the Cleveland Clinic score, the French score, the Pons score, the Ontario Province score, the Society of Thoracic Surgery (STS) Scoring System, the EuroSCORE and the Bernstein-Parsonnet.Although there is not an ideal risk stratification model, this should have the following characteristics: ease of implementation, objectivity, accuracy in the prediction of mortality and have widespread use. I a recently published meta-analysis
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 | 0.075 | 0.257 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".