Identifying Cancer Subjects With Acute Respiratory Failure at High Risk for Intubation and Mechanical Ventilation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: We sought to identify risk factors for mechanical ventilation in patients with malignancies and acute respiratory failure (ARF). METHODS: We analyzed data from a previous randomized controlled trial in which nonintubated oncology and hematology subjects with ARF were randomized to early bronchoalveolar lavage or routine care in 16 ICUs in France. Consecutive patients with malignancies were admitted to the ICU for ARF in 2005 and 2006 with no intervention. RESULTS: During the study period, 219 patients were admitted to the ICU for ARF, and 8 patients were not included due to a nonintubation order. Data on the underlying disease, pulmonary involvement, and extrapulmonary organ dysfunctions were recorded at admission in the 211 remaining subjects. Ventilatory support included oxygen only (49 subjects), noninvasive ventilation (NIV) only (81 subjects), NIV followed by invasive mechanical ventilation (49 subjects), and first-line invasive mechanical ventilation (32 subjects). The 81 subjects who required invasive mechanical ventilation were compared with the 130 subjects who remained on oxygen or NIV. Factors associated with invasive mechanical ventilation by multivariate analysis were the oxygen flow required at ICU admission, the number of quadrants involved on chest x-ray, and hemodynamic dysfunction. Mortality rates for subjects who had NIV failure were 65.3% compared with 50% for subjects who were first-line intubated (P = .34). CONCLUSIONS: In cancer patients with ARF, hypoxemia, extent of pulmonary infiltration on chest x-ray, or hemodynamic dysfunction are risk factors for invasive mechanical ventilation. Mortality was not significantly different between NIV failure and first-line intubation.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 it