Evaluating the test re-test reliability and inter-subject variability of health care provider manual fluid resuscitation performance
Bibliographic record
Abstract
BACKGROUND: Health Care Providers (HCPs) report that manual techniques of intravascular fluid resuscitation are commonly used during pediatric shock management. The optimal pediatric fluid resuscitation technique is currently unknown. We sought to determine HCP test-retest reliability (repeatability) and inter-subject variability of fluid resuscitation performance outcomes to inform the design of future studies. METHODS: Fifteen consenting HCPs from McMaster Children's Hospital, in Hamilton, Canada participated in this single-arm interventional trial. Participants were oriented to a non-clinical model representing a 15 kg toddler, which incorporated a 22-gauge IV catheter. Following a standardization procedure, participants administered 600 mL (40 mL/kg) of saline to the simulated child under emergency conditions using prefilled 60-mL syringes. Each participant completed 5 testing trials. All testing was video recorded, with fluid administration time outcome data (in seconds) extracted from trial videos by two blinded outcome assessors. Data describing catheter dislodgement events, volume of saline effectively delivered, and participant demographics were also collected. The primary outcome of fluid administration time test-retest reliability was analyzed by one-way analysis of variance (ANOVA) and intra-class correlation (ICC), with good reliability defined as ICC > 0.70. RESULTS: Differences in HCP fluid administration times are attributable to inter-subject variability rather than intra-subject variability based on one-way ANOVA analysis, F (14,60) = 43.125; p < 0.001. Test-retest reliability of subjects was excellent with ICC = 0.97 (95% CI: 0.95-0.99); p < 0.001. CONCLUSIONS: Findings demonstrate excellent test-retest reliability of HCP fluid resuscitation performance in a setting involving a non-clinical model. Investigators can justify a single evaluation of HCP performance in future studies.
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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 | 0.062 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".