High rate of missing vital signs data at triage in a paediatric emergency department
Bibliographic record
Abstract
BACKGROUND: Vital signs measurement is considered standard practice in paediatric emergency department triage assessment, but studies have shown variable incidence of missing data. OBJECTIVES: To evaluate the rate of missing data for vital signs at triage and to determine clinical and environmental predictive factors. METHODS: A retrospective cohort design was used to study a database of consecutive patients registered at a tertiary paediatric emergency department during randomly chosen shifts. Demographic and clinical data were collected. Univariate and multivariate logistic regression analyses were performed to evaluate the determinants of missing data for body temperature, heart rate, respiratory rate, blood pressure and pulse oximetry. RESULTS: There were 2081 patients triaged during the study periods. On multivariate logistic regression analysis, triage level (from 1 = priority to 4 = nonurgent) was an independent predictor of missing data for heart rate, respiratory rate, blood pressure and pulse oximetry (OR 1.48 to 2.05). Patients visiting the emergency department during the day shift (OR 1.08 to 4.72) and the evening shift (OR 1.38 to 9.24) had a higher rate of missing data than those visiting during the night shift. A decreased level of consciousness, an immunocompromised state and referral by a physician did not meet statistical significance as predictive factors. CONCLUSIONS: There was a high rate of missing data for vital signs. Factors related to patients' clinical characteristics, such as acuity of triage level, were associated with a higher rate of vital signs documentation at triage. An environmental factor, shift of presentation, was also independently associated with a higher rate.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".