EVIDENCE OF "ESSENTIAL UNCERTAINTY" IN EMERGENCY-WARD LENGTH OF STAY
Bibliographic record
Abstract
Scaling in random time series is a novel way of gaining insight into mechanisms of complex systems. Scaling parameters characterize searches for the presence of "essential" uncertainty in outcomes of complex systems. The presence of this type of uncertainty in the emergency system (ES) can point to unusual ways of relieving the financial burden to the health system and improve the public health. The objective of this paper is to test for the presence of "essential" uncertainty in an outcome variable of the ES, i.e. emergency-ward length of stay (EWLS). An inverse power law (IPL) function fit to data can assess "essential" uncertainty. The study of EWLS has been undertaken in ten large Quebec hospitals. We find that all hospital EWLS are well fit by an IPL, as determined by an aggregated allometric relation. The presence of "essential" uncertainty is further confirmed by stratifying the EWLS according to proxies of disease complexity and severity at entry in emergency ward. Results point out that the various hospital dynamic systems embed "essential" uncertainty to various degrees. We conclude that intervention to reduce hospital health care costs must be centered on the interaction and feedback characterizing the ES processing of the patients.
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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 | 0.008 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".