Changes in Patterns of Referral for Inpatient Rehabilitation Cancer Patients Due to COVID-19
Bibliographic record
Abstract
ABSTRACT: There is a paucity of literature on the effect of COVID-19 on hospital processes. We hypothesized that COVID-19 was associated with decreased cancer physiatry referrals in 2020. This is a retrospective cohort study of consecutive patients from April to July 2019 and 2020 admitted at an academic quaternary cancer center. The main outcomes were number of hospital admissions, rate, and characteristics of inpatient rehabilitation admissions and change in percentage of physiatry referrals as the primary endpoint. Results showed that in 2019, there were 387 referrals from 10,274 inpatient admissions (3.8%; 95% confidence interval, 2.4-4.2), compared with 337 referrals from 7051 admissions in 2020 (4.8%; 95% confidence interval, 4.3-5.3, P = 0.001). Hematology services referred more patients than neurosurgery in 2020 (20.4% vs. 31.4%; 48.2% vs. 26.5%, P = 0.01). Discharge disposition reflected an increased frequency of return to acute care service in 2020 (10.2% vs. 21.8%, P = 0.03). In conclusion, there was an increase in the rate of physiatry referrals despite a decrease in hospital admissions. There was an increase in referrals by hematology, likely due to emphasis on safe discharge and the populations hospitalized.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".