Flow-directed vs. goal-directed strategy for management of hemodynamics
Bibliographic record
Abstract
PURPOSE OF REVIEW: For the past 15 years, there has been a strong push to use goal-directed protocols for resuscitating critically ill patients and to manage perioperative patients. However, recent large clinical trials have failed to find evidence of improved outcome with this approach. RECENT FINDINGS: A striking feature in the recent three large prospective randomized trials of septic patients and the one in high-risk perioperative patients is that outcomes in the control groups have markedly improved. This implies improvement in care and clinical acumen. Perhaps the clinical approach should be more toward further helping clinicians with their clinical choices. A good example is cardiac output. The objective of most hemodynamic interventions is to increase cardiac output. It would thus make sense to assess what happened to cardiac output after the intervention to determine if the intervention actually increased cardiac output. If it did not, another therapy should be chosen. I call this a flow-directed responsive protocol. SUMMARY: A clinical approach that uses monitored values such as cardiac output as a feedback tool to evaluate the response to therapeutic interventions in individual patients may be better than protocols that set fixed targets for all study participants.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".