A population-based analysis of outcomes after radiotherapy in intensive care unit patients with lung cancer
Bibliographic record
Abstract
Background: As the value of radiotherapy (RT) in intensive care unit (ICU) patients with lung cancer is of uncertain efficacy, we evaluated characteristics, outcomes and RT utilization for such patients in Ontario, Canada. Methods: Multiple administrative databases were linked deterministically using unique encoded identifiers to identify eligible patients between April 1, 2007, and March 31, 2014. Differences in patient, treatment, institution and tumor characteristics between RT and non-RT groups at the level of episode of care were compared. Overall survival (OS) was evaluated using the Kaplan-Meier method, with differences compared using the log-rank test. Univariable and multivariable Cox proportional hazard modeling were performed to assess the effect of RT on survival. Results: RT was delivered in 133 episodes of care to 1.0% (n=131) of the 13,739 unique patients with lung cancer. RT delivery was associated with younger age (median 65 vs. 68, P<0.001), ventilation (79.8% vs. 38.2%, P<0.001) and longer ventilation duration (median 6 vs. 0 days, P<0.001). Pre-ICU disposition via transfer (35.3% vs. 9.7%) or the emergency room (ER) (28.6% vs. 21.9%) was more likely in the RT group (P<0.001). RT delivery varied, with half of the regions treating ≤5 patients each. ICU discharge was common in both RT (n=75, 56.4%) and non-RT (n=10,405, 71.4%) cohorts. One-year OS was poor in both groups, but most notably in the RT group (11.3% vs. 42.4%). RT was associated with inferior 1-year OS on unadjusted modeling (HR =1.99, P<0.001), with ventilation and pre-ICU disposition adjusting this finding towards the null on multivariable modeling (HR =1.17, P=0.095). Conclusions: Major geographic disparities exist in the rare use of RT for lung cancer in the ICU. A significant proportion of patients receiving RT achieve discharge and a minority achieve prolonged survival, suggesting that RT use may not be futile.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".