One-Year Readmission Following Undifferentiated Acute Hypercapnic Respiratory Failure
Bibliographic record
Abstract
Patients with acute hypercapnic respiratory failure (AHRF) often require hospitalization and respiratory support. Early identification of patients at risk of readmission would be helpful. We evaluated 1-y readmission and mortality rates of patients admitted for undifferentiated AHRF and identified the impact of initial severity on clinically important outcomes. We retrospectively analyzed patients who presented with AHRF to the emergency department of St Michael's Hospital in 2017. We collected data about patients' characteristics, hospital admission, readmission and mortality one year after the index admission. We analyzed predictors of readmission and mortality and conducted a survival analysis comparing patients who did and did not receive ventilatory support. A cohort of 212 patients with AHRF who survived their hospital admission were analyzed. At one year, 150 patients (70.8%) were readmitted and 19 (9%) had died. Main diagnoses included chronic obstructive pulmonary disease (60%), congestive heart failure (36%), asthma (22%) and obesity (19%), and these categories of patients had similar 1 y readmission rates. One third had more than one coexisting chronic illness. Although comorbidities were more frequent in readmitted patients, only a history of previous hospital admissions remained associated with 1 y readmission and mortality in multivariate analysis. Need for ventilatory support at admission was not associated with higher 1 y probability of readmission or death. Undifferentiated AHRF is the presentation of multiple chronic illnesses. Patients who survive one episode of AHRF and with previous history of admission have the highest risk of readmission and death regardless of whether they receive ventilatory support during index admission.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".