Resuscitation Strategies in Early Septic Shock: A Survey of Puerto Rico Intensive Care Physicians.
Bibliographic record
Abstract
OBJECTIVE: Severe sepsis and Septic Shock may progress in the first hours after presentation and has been associated with an increased mortality. Prompt recognition and treatment of early septic shock (ESS) may improve survival. The purpose of our study was to describe the monitoring and management strategies of ESS, within Intensive Care Units (ICU) in Puerto Rico (PR). METHODS: In order to achieve our objective, a self-administered survey, previously validated by the Canadian Critical Care Trials Group, was administered to 25 physicians during a Critical Care Medicine (CCM) Meeting. Questions about usual monitoring and resuscitation end-points were administered. RESULTS: Most of the participants were affiliated to community hospitals (84%) and 92% were pulmonary or CCM specialists, with more than 15 years of working experience (80%). Monitoring devices and parameters mostly used by at least 85% of the respondents were: Oxygen Saturation, Foley catheters, Telemetry, Heart Rate, Blood Pressure, and Urinary Output. Intra-arterial lines and Central Venous Pressure were less used. Most use normal saline (96%), as the initial fluid of resuscitation. Only 24% would use inotropes to improve perfusion. CONCLUSION: Significant variability exists in the management of ESS among physicians in the ICU in PR. Compared to other studies, fewer physicians in PR use invasive monitoring techniques. These results highlight the need for quality education and training in CCM as well as continuing education in the field.
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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 | 0.000 | 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) | 0.001 | 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 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".