Invasive mechanical ventilation duration prediction using survival analysis
Bibliographic record
Abstract
This study analyzed first-day ventilated patients' ventilation time using survival analysis. A retrospective analysis of ICU ventilation time in Ontario was performed with data from ICU patients obtained from the Critical Care Information System (CCIS. Parametric survival methods were used to characterize ventilation time and to determine associated covariates. Parametric and non-parametric methods were used to determine predictors of ventilation duration for first-day ventilated patients. Analysis of ventilation duration suggested that the log-normal distribution provided the best fit for ventilation time, whereas the log-logistic Accelerated Failure Time model best describes the association between the covariates and ventilation duration. ICU site, admission source, admission diagnosis, scheduled admission, scheduled surgery, referring physician, central venous line treatment, arterial line treatment, intracranial pressure monitor treatment, extra-corporeal membrane oxygen treatment, intra-aortic balloon pump treatment, other interventions, age group, pre-ICU length of stay (LOS), and multiple organ dysfunction syndrome (MODS) scores were significant predictors of the ICU ventilation time. The results show substantial variability in ICU ventilation duration for different ICUs, patient demographics, and underlying conditions and highlight mechanical ventilation as an important driver of ICU costs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".