The PIRO (predisposition, insult, response, organ dysfunction) model
Bibliographic record
Abstract
Multimodal therapy for diseases like cancer has only become practicable following the development of staging systems like the TNM (tumor, nodes, metastases) system. Staging enables the identification of subgroups of patients with a disease who not only have a differing prognosis, but who are also more likely to benefit from a specific therapeutic modality. Critically ill patients represent a highly heterogeneous population for whom multiple therapeutic options are potentially available, each carrying not only the potential for differential benefit, but also the potential for differential harm. The PIRO system (predisposition, insult, response, organ dysfunction) is a template proposal for a staging system for acute illness that incorporates assessment of pre-morbid baseline susceptibility (predisposition), the specific disorder responsible for acute illness (insult), the response of the host to that insult, and the resulting degree of organ dysfunction. However the creation of a valid, robust, and clinically useful system presents significant challenges arising from the complexity of the disease state, the lack of a clear phenotype, the confounding influence of the effects of therapy and of cultural and socio-economic factors, and the relatively low profile of acute illness with clinicians and the general public. This review summarizes the rationale for such a model of illness stratification and the results of preliminary cohort studies testing the concept. It further proposes two strategies for building a staging system, recognizing that this will be a demanding undertaking that will require decades of work.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".