Access to Palliative Care for Patients Undergoing Mechanical Ventilation With Idiopathic Pulmonary Fibrosis in the United States
Bibliographic record
Abstract
OBJECTIVE: The utilization of palliative care (PC) in patients with end-stage idiopathic pulmonary fibrosis (IPF) is not well understood. METHODS: The Nationwide Inpatient Sample (NIS) was utilized to examine the use of PC in mechanically ventilated (MV) patients with IPF. The NIS captures 20% of all US inpatient hospitalizations and is weighted to estimate 95% of all inpatient care. RESULTS: A total of 55 208 382 hospital admissions from the 2006 to 2012 NIS samples were examined. There were 21 808 patients identified with pulmonary fibrosis, of which 3166 underwent mechanical ventilation and were included in the analysis. Of the 3166 patients in the main cohort, 408 (12.9%) had an encounter with PC, whereas 2758 (87.1%) did not. After multivariate logistic regression modeling, variables associated with increased access to PC referral were age (odds ratio [OR]: 1.02, 95% confidence interval [CI]: 1.01-1.03, P < .01), treatment in an urban teaching hospital (OR: 1.49, 95% CI: 1.27-3.58, P < .01), and do-not-resuscitate status (OR: 9.86, 95% CI: 7.48-13.00, P < .01). Factors associated with less access to PC were Hispanic race (OR: 0.64, 95% CI: 0.41-0.99, P = .04) and missing race (OR: 0.52, 95% CI: 0.34-0.79, P < .01), with white race serving as the reference. The use of PC has increased almost 10-fold from 2.3% in 2006 to 21.6% in 2012 ( P < .01). CONCLUSION: The utilization of PC in patients with IPF who undergo MV has increased dramatically between 2006 and 2012.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".