Learning to prescribe intravenous fluids: A scoping review
Bibliographic record
Abstract
INTRODUCTION: Prescribing intravenous (IV) fluid therapy is a core skill expected of qualified doctors at the point of graduation, but medical graduates often feel ill-equipped to perform this task. This lack of preparedness contributes to treatment-related patient harm. This scoping review maps the current state of published evidence about how junior doctors prescribe IV fluid therapy and learn how to do it. METHODS: We searched five electronic databases and grey literature from 1994 until June 2016 for articles describing any aspect of IV fluid prescribing practice or its education. A total of 63 articles were selected for analysis. Using the WHO Guide to Good Prescribing to categorize the extracted findings, our review focuses on prescribing IV fluids in adult generalist settings. RESULTS: Most articles studied IV fluid prescribing from the perspective of the doctor. Junior clinicians struggled to conceptualize IV fluid prescribing as a 'whole task' in authentic work settings and lacked support. Educational interventions to improve IV fluid prescribing often focused on enhancing prescriber knowledge about fluid and electrolyte balance rather than execution of the prescribing task. CONCLUSIONS: Our understanding of IV fluid prescribing as a holistic integrated skill is patchy, as is its performance. Current IV fluid prescribing education appears insufficient to foster safe and effective practice. For education to achieve the ultimate goal of safer prescribing in workplaces, we need a clearer understanding of how healthcare professionals prescribe IV fluids in real world practice.
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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 | 0.009 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".