Clinician Prediction of Survival vs Calculated Prediction Scores in Patients Requiring Extracorporeal Membrane Oxygenation
Bibliographic record
Abstract
BACKGROUND: Determining appropriate extracorporeal membrane oxygenation (ECMO) candidacy ensures appropriate utilization of this costly resource. The current ECMO survival prediction scores do not consider clinician assessment of patient viability. This study compared clinician prediction of survival to hospital discharge versus prediction scores. OBJECTIVES: The aim of this study was to compare clinician prediction of patients' survival to hospital discharge versus prognostic prediction scores (Respiratory ECMO Survival Prediction [RESP] or Survival After Veno-Arterial ECMO [SAVE] score) to actual survival. METHODS: This was an observational descriptive study from January 2020 to November 2021 conducted with interviews of nurses, perfusionists, and physicians who were involved during the initiation of ECMO within the first 24 hours of cannulation. Data were retrieved from the medical record to determine prediction scores and survival outcomes at hospital discharge. Accuracy of clinician prediction of survival was compared to the RESP or SAVE prediction scores and actual survival to hospital discharge. RESULTS: Accurate prediction of survival to hospital discharge for veno-venous ECMO by nurses was 47%, 64% by perfusionists, 45% by physicians, and 45% by the RESP score. Accurate predictions of patients on veno-arterial ECMO were correct in 54% of nurses, 77% of physicians, and 14% by the SAVE score. Physicians were more accurate than the SAVE score, P = .021, and perfusionists were significantly more accurate than the RESP score, P = .044. There was no relationship between ECMO specialists' years of experience and accuracy of predications. CONCLUSION: Extracorporeal membrane oxygenation clinicians may have better predictions of survival to hospital discharge than the prediction scores. Further research is needed to develop accurate prediction tools to help determine ECMO eligibility.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".