Understanding Local Consultation Patterns of Inpatient Geriatric Medicine Teams: a Cross-Sectional Study
Bibliographic record
Abstract
Background: Geriatric consultation for Comprehensive Geriatric Assessment (CGA) improves outcomes of older adults living with frailty who are hospitalized, but consultation patterns and utilization of inpatient geriatric consultation teams by other hospital-based services are poorly understood. Methods: We conducted a cross-sectional study using linked health administrative data to describe characteristics of older adults (≥ 65 years) who received a CGA while hospitalized between January 1, and December 31, 2019. We identified hospital-based services requesting CGA and the frequency and reasons for referral. We used multivariable logistic regression to estimate the association between patient-level characteristics and receiving a CGA. Results: A total of 29,090 older adults were admitted to hospital; 38.7% were classified as frail and 5.4% (1,563 patients) received at least one CGA. The top three reasons for requesting a CGA were to assess the need for care on an inpatient geriatric rehabilitation unit (43%), and for assessment and management of delirium (27%) and dementia (24%). Referrals were most frequently received from Hospitalists (48%). Frailty was associated with increased odds of receiving a CGA (adjusted odds ratio [aOR] 12.02; 95% confidence interval [CI] 9.67-14.82). A diagnosis of cancer was associated with lower odds of receiving a CGA (aOR 0.75; 95% CI 0.60-0.93). Conclusions: Inpatient geriatric consultation teams support 5.4% of hospitalized older adults. With the rapidly growing aging population, future efforts are needed to explore the optimal delivery of inpatient geriatric services to support its sustainable provision.
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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".