Ten reasons why we should NOT use severity scores as entry criteria for clinical trials or in our treatment decisions*
Bibliographic record
Abstract
OBJECTIVE: Severity scores such as Acute Physiology and Chronic Health Evaluation II have been advocated as entry criteria for clinical trials and in clinical decision-making. We present ten reasons why we believe this approach is not appropriate and may even be detrimental. DATA SOURCES: Available relevant literature from authors' personal databases and personal knowledge of past and future clinical trial development. DATA SYNTHESIS: Severity scores were not designed for use in individual patients or for therapeutic decision-making for specific interventions. Difficulties with the time window needed to calculate these scores and the need to administer therapies early further limit their use in this context. The complex nature of the scores makes it difficult to use them at the bedside and there is considerable interobserver variability in score calculation. Inclusion of chronic health and age points in severity scores may prevent younger, previously healthy patients, with similar acute physiological dysfunction and therefore total lower severity scores, from trial inclusion or from receiving therapies that may be beneficial. CONCLUSIONS: We believe severity of illness scores are poor surrogates for risk stratification and should not be used as a criterion for patient enrollment into clinical trials or as the basis for individual treatment decisions.
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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 | 0.306 | 0.365 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".