Comparing characteristics and outcomes of cancer to non-cancer patients admitted to general internal medicine (GIM).
Bibliographic record
Abstract
21 Background: Cancer prevalence is rising and there is a corresponding increase in hospitalizations across the cancer continuum. However, little is known about the patterns of care and outcomes of cancer inpatients as administrative data may not capture in-hospital details including investigations and medications required for characterization. Understanding how cancer inpatients are managed and their outcomes can help to optimize care delivery. Methods: We conducted a multicenter study of all patients admitted to GIM at seven hospitals (Toronto, Canada) from 2010 to 2017 where we deterministically linked administrative data with each hospital’s electronic information (pharmacy, orders, notes, laboratory/imaging and results) at the patient level. Multivariable regression models compared characteristics and outcomes between cancer and non-cancer patients for the top 5 non-cancer patient discharge diagnoses. Results: Among 230,040 hospitalizations, 15% had cancer listed as an ICD-10 comorbidity. The most common cancer disease sites were gastrointestinal (20%), lung (13%) and leukemia (11%). The most common discharge diagnoses for cancer patients were disease progression (9%), palliative care (6%), pneumonia (4%), leukemia (4%) and lung cancer (4%), while for non-cancer patients were: heart failure (5%), pneumonia (5%), stroke (5%), COPD (5%) and urinary tract infections (5%). In general, compared to non-cancer patients, cancer patients were younger (70 vs 72), had greater length of stay (LOS; 6.4 vs 4.6 days), in-hospital mortality (16% vs 5%), ICU use (12% vs 11%), 30 day re-admission rate (17% vs 10%) and were more likely to receive CTs (64% vs 52%), MRIs (14% vs 12%) and interventional procedures (22% vs 8%) (p < 0.001, all comparisons). When evaluating the top 5 non-cancer patient discharge diagnoses, results (adjusted for age, gender, Charlson comorbidity score and hospital) were similar wherein cancer patients had a higher in-hospital mortality (aOR = 2.02 p < 0.001), 30 day re-admission rate (aOR = 1.09 p = 0.08) and were more likely to receive CTs (aOR = 1.88 p < 0.001), MRIs (aOR = 1.66 p < 0.001) or interventional procedures (aOR = 1.78 p < 0.001), despite similar mean LOS (5.7 vs 5.1 days p = 0.35). Results were similar across discharge diagnoses. Conclusions: Cancer patients represent a unique population on GIM and have higher resource use, mortality and LOS compared to non-cancer patients, with similar trends even for the same non-cancer diagnoses. Specialized models of care for hospitalized cancer patients may be warranted.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".