Waterlow score for risk assessment in surgical patients: a systematic review
Bibliographic record
Abstract
INTRODUCTION: The Waterlow score (WS) is used routinely in clinical practice to assess risk of pressure sore development. Recent studies have also suggested its use in preoperative risk stratification. The primary aim of this systematic review was to evaluate the current evidence on the WS in predicting morbidity and mortality in surgical patients. METHODS: A systematic review was carried out in accordance with PRISMA and SWiM guidelines. A search strategy was conducted on the MEDLINE and EMBASE databases. Quality was assessed using the Newcastle-Ottawa scale. FINDINGS: Overall, 72 papers were identified, of which 7 met inclusion criteria for full text review, and 4 were included for analysis. All studies were cohort in nature and published between 2013 and 2016, encompassing a total of 505 surgical patients. The studies included general, vascular, transplant and orthopaedic surgery. A high WS was demonstrated to have statistically significant association with increased morbidity and mortality as well as need for intensive care unit admission and length of stay. Furthermore, this was a more accurate predictor compared with the P-POSSUM and ASA scoring systems used currently in routine practice. CONCLUSIONS: The WS is a promising tool for risk stratification of surgical patients. It is already collected routinely by nursing staff throughout hospitals in the UK and would therefore be easy to implement. However, further large prospective studies are required in order to validate these findings prior to its establishment for this role in everyday surgical practice.
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How this classification was reachedexpand
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".