The time cost of physiologically ineffective intravenous fluids in the emergency department: an observational pilot study employing wearable Doppler ultrasound
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Background Little data exist on the time spent by emergency department (ED) personnel providing intravenous (IV) fluid to ‘responsive’ versus ‘unresponsive’ patients. Methods A prospective, convenience sample of adult ED patients was studied; patients were enrolled if preload expansion was indicated for any reason. Using a novel, wireless, wearable ultrasound, carotid artery Doppler was obtained before and throughout a preload challenge (PC) prior to each bag of ordered IV fluid. The treating clinician was blinded to the results of the ultrasound. IV fluid was deemed ‘effective’ or ‘ineffective’ based on the greatest change in carotid artery corrected flow time (ccFT ∆ ) during the PC. The duration, in minutes, of each bag of IV fluid administered was recorded. Results 53 patients were recruited and 2 excluded for Doppler artifact. There were 86 total PCs included in the investigation comprising 81.7 L of administered IV fluid. 19,667 carotid Doppler cardiac cycles were analyzed. Using ccFT ∆ ≥ + 7 ms to discriminate ‘physiologically effective’ from ‘ineffective’ IV fluid, we observed that 54 PCs (63%) were ‘effective’, comprising 51.7 L of IV fluid, whereas, 32 (37%) were ‘ineffective’ comprising 30 L of IV fluid. 29.75 total hours across all 51 patients were spent in the ED providing IV fluids categorized as ‘ineffective.’ Conclusions We report the largest-known carotid artery Doppler analysis (i.e., roughly 20,000 cardiac cycles) in ED patients requiring IV fluid expansion. A clinically significant amount of time was spent providing physiologically ineffective IV fluid. This may represent an avenue to improve ED care efficiency.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it