Commentary Why aren’t we practising homogenized medicine?
Bibliographic record
Abstract
Why is the practice of intensive care so heterogenous? Uncertainty as to ‘best practice’, conservatism, and complacency may all contribute to our divergent management strategies. The need for further generalisable research, anonymised audit, external peer review and open access databases is discussed. Lauralyn McIntyre and colleagues [1] have neatly used a septic shock scenario-based survey to highlight considerable variations within Canadian critical care practice. They acknowledge the potential pitfalls of translating survey results into ‘real life’; however, my own experience of the diversities within UK practice suggest this would be representative of at least one other industrialized country, albeit with some variation in the detail (for example, use of gelatin as a plasma expander is much commoner in Europe). They found decisions regarding treatment strategy (choice of fluid, use of inotropes and transfusion triggers) to be highly variable. However, they did demonstrate consistency in a continuing reliance on ‘basic ’ monitoring (blood pressure, heart rate, central venous pressure, urine output, pulse oximetry). This was to the relative exclusion of other, more sophisticated techniques (cardiac output, central venous saturation) whose use has been linked with outcome improvements in specific situations, such as the scenario on which their survey was based. Is this heterogeneity a triumph of uncertainty and/or natural conservatism and/or arrogance and/or sloth over heavily promoted, multiple Society-endorsed guidelines [2] based primarily on the important yet limited Rivers study [3]? Why aren’t we all practising homogenized medicine? What does it take to standardize our approach to care of the critically ill? Uncertainty does exert a considerable effect. The Institute of
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.
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 | 0.016 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.041 | 0.041 |
| Insufficient payload (model declined to judge) | 0.031 | 0.014 |
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".