The Withdrawal Assessment Tool to identify iatrogenic withdrawal symptoms in critically ill paediatric patients: A<scp>COSMIN</scp>systematic review of measurement properties
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: The Withdrawal Assessment Tool (WAT-1) is one of the most widely used clinician-reported outcome measures to evaluate iatrogenic withdrawal symptoms (IWS) in critically ill children. However, the WAT-1's measurement properties have not been aggregated. Aggregating psychometric research on the WAT-1 will enhance appropriate use, and outline gaps for future empirical research. The aim of this systematic review is to critically appraise, compare, and summarize the measurement properties and evidence quality, and describe the interpretability and feasibility of the WAT-1 for identifying IWS symptoms in critically ill children. METHODS: A systematic search of Medline, Embase and CINAHL was conducted from inception to 15 April 2020. Study inclusion/exclusion, data extraction, and measurement property evidence and the modified GRADE quality scoring were applied according to the COnsensus-based Standards for the selection of health Measurement Instruments (COSMIN) guidelines. RESULTS: Six studies were included in the review. There was sufficient, high-quality evidence for reliability, structural validity, criterion validity, measurement error, construct validity, and feasibility. More information is required to support the WAT-1's content validity, responsiveness, internal consistency, cross-cultural validity, and interpretability according to COSMIN guidelines. CONCLUSION: The results of this review indicate that the WAT-1 is a precise, easy to use measure of IWS in critically ill children despite some measurement property inconsistencies and gaps in the publication record. More information is required to support its content validity, responsiveness, internal consistency, cross-cultural validity, and interpretability.
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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 | 0.026 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".