An argument for using additional bedside tools, such as bedside ultrasound, for volume status assessment in hospitalized medical patients: A needs assessment survey
Bibliographic record
Abstract
The frequency at which housestaff need to assess volume status on medical inpatients is unknown. In this brief report, we invited 39 housestaff, over 13 randomly selected dates, to complete a 25-item survey. Participants (n = 31, 79%) logged a total of 455 hours, reporting 197 pages or telephone requests received regarding medical inpatients. Of these, 41 pages (21%) required a volume status assessment. Participants reported their volume status assessment competency to be moderate (median score = 3, interquartile range = 3 to 4, where 1 = not competent to perform independently and 6 = above average competence). In 9 of the 41 assessments (22%), at least 1 barrier was reported in determining volume status. The most commonly reported barriers were conflicting physical examination findings (n = 8, 20%) and suboptimal patient examination (n = 5, 12%). Over 20% of pages regarding admitted medical patients required volume status assessments by medical housestaff. Despite moderate self-reported competence in the ability to assess volume status, barriers such as conflicting physical examination findings and suboptimal patient examinations were present in up to 20% of assessments. Therefore, we urge educators to consider incorporating bedside ultrasound training for volume status into the internal medicine curriculum.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".