Early Warning Scores and Their Application in the Inpatient Oncology Settings
Bibliographic record
Abstract
Early Warning Score (EWS) systems are tools that use alterations in vital signs to rapidly identify clinically deteriorating patients and escalate care accordingly. Since its conception in 1997, EWSs have been used in several settings, including the general inpatient ward, intensive care units, and the emergency department. Several iterations of EWSs have been developed with varying levels of sensitivity and specificity for use in different populations. There are multiple strengths of these tools, including their simplicity and their ability to standardize communication and to reduce inappropriate or delayed referrals to the intensive care unit. Although early identification of deteriorating patients in the oncology population is vital to reduce morbidity and mortality and to improve long-term prognosis, the application in the oncology setting has been limited. Patients with an oncological diagnosis are usually older, medically complex, and can have increased susceptibility to infections, end-organ damage, and death. A search using PubMed and Scopus was conducted for articles published between January 1997 and November 2020 pertaining to EWSs in the oncology setting. Seven relevant studies were identified and analyzed. The most commonly used EWS in this setting was the Modified Early Warning Score. Of the seven studies, only two included prospective validation of the EWS in the oncology population and the other five only included a retrospective assessment of the data. The majority of studies were limited by their small sample size, single-institution analysis, and retrospective nature. Future studies should assess dynamic changes in scores over time and evaluate balance measures to identify use of health care resources.
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 | 0.012 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.024 | 0.025 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".