Ten reasons why we should NOT use severity scores as entry criteria for clinical trials or in our treatment decisions*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.344 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it