Decreasing hospitalizations through geriatric hotlines: a prospective French multicenter study of people aged 75 and above
Bibliographic record
Abstract
BACKGROUND: The Emergency unit of the hospital (Department) (ED) is the fastest and most common way for most French general practitioners (GPs) to respond to the complexity of managing older adults patients with multiple chronic diseases. In 2013, French regional health authorities proposed to set up telephone hotlines to promote interactions between GP clinics and hospitals. The main objective of our study was to analyze whether the hotlines and solutions proposed by the responding geriatrician reduced the number of hospital admissions, and more specifically the number of emergency room admissions. METHODS: We conducted a multicenter observational study from April 2018 to April 2020 at seven French investigative sites. A questionnaire was completed by all hotline physicians after each call. RESULTS: The study population consisted of 4,137 individuals who met the inclusion and exclusion criteria. Of the 4,137 phone calls received by the participants, 64.2% (n = 2 657) were requests for advice, and 35.8% (n = 1,480) were requests for emergency hospitalization. Of the 1,480 phone calls for emergency hospitalization, 285 calls resulted in hospital admission in the emergency room (19.3%), and 658 calls in the geriatric short stay (44.5%). Of the 2,657 calls for advice/consultation/delayed hospitalization, 9.7% were also duplicated by emergency hospital admission. CONCLUSION: This study revealed the value of hotlines in guiding the care of older adults. The results showed the potential effectiveness of hotlines in preventing unnecessary hospital admissions or in identifying cases requiring hospital admission in the emergency room. Hotlines can help improve the care pathway for older adults and pave the way for future progress. TRIAL REGISTRATION: Registered under Clinical Trial Number NCT03959475. This study was approved and peer-reviewed by the Ethics Committee for the Protection of Persons of Sud Est V of Grenoble University Hospital Center (registered under 18-CETA-01 No.ID RCB 2018-A00609-46).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".