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Record W3006583962 · doi:10.2196/15423

Impact of Geriatric Hotlines on Health Care Pathways and Health Status in Patients Aged 75 Years and Older: Protocol for a French Multicenter Observational Study

2020· article· en· W3006583962 on OpenAlexaffvenue
Laure Martinez, Noémie Lacour, Régis Gonthier, Marc Bonnefoy, Luc Goethals, Cédric Annweiler, Nathalie Salles, Nathalie Jomard, Jérôme Bohatier, Magali Tardy, Étienne Ojardias, Romain Jugand, Bienvenu Bongué, Thomas Célarier

Bibliographic record

VenueJMIR Research Protocols · 2020
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsWestern University
Fundersnot available
KeywordsHotlineMedicineGeriatricsObservational studyReferralFamily medicineHealth careGerontologyMedical emergencyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: In France, emergency departments (EDs) are the fastest and most common means for general practitioners (GPs) to cope with the complex issues presented by elderly patients with multiple conditions. EDs are overburdened, and studies show that being treated in EDs can have a damaging effect on the health of elderly patients. Outpatient care or planned hospitalizations are possible solutions if appropriate geriatric medical advice is provided. In 2013, France's regional health authorities proposed creating direct telephone helplines, "geriatric hotlines," staffed by geriatric specialists to encourage interactions between GP clinics and hospitals. These hotlines are designed to improve health care pathways and the health status of the elderly. OBJECTIVE: This study aims to describe the health care pathways and health status of patients aged 75 years and older hospitalized in short-stay geriatric wards following referral from a geriatric hotline. METHODS: The study will be conducted over 24 months in seven French university hospital centers. It will include all patients aged 75 and older, living in their own homes or nursing homes, who are admitted to short-stay geriatric wards following hotline consultation. Two questionnaires will be filled out by medical staff at specific time points: (1) after conducting the telephone consultation and (2) on admitting the patient to a short-stay geriatric medical care. The primary endpoint will be mean hospitalization duration. The secondary endpoints will be intrahospital mortality rate, the characteristics of patients admitted via the hotline, and the types of questions asked and responses given via the hotline. RESULTS: The study was funded by the National School for Social Security Loire department (École Nationale Supérieure de Sécurité Sociale) and the Conference for funders of prevention of autonomy loss for the elderly of the Loire department in November 2017. Institutional review board approval was obtained in April 2018. Data collection started in May 2018; the planned end date for data collection is May 2020. Data analysis will take place in the summer of 2020, and the first results are expected to be published in late 2020. CONCLUSIONS: The results will reveal whether geriatric hotlines provide the most effective management of elderly patients, as indicated by shorter mean hospitalization durations. Shorter hospital durations could lead to a reduced risk of complications-geriatric syndromes-and the domino chain of geriatric conditions that follow. We will also describe different geriatric hotlines from different cities and compare how they function to improve the health care of the elderly and pave the way toward new advances, especially in the organization of the care path. TRIAL REGISTRATION: ClinicalTrials.gov NCT03959475; https://clinicaltrials.gov/ct2/show/NCT03959475. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/15423.

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 imitation

Not 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.

metaresearch head score (Codex)0.030
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.030
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.014
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0050.006
Bibliometrics0.0030.004
Science and technology studies0.0030.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0120.002

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.

Opus teacher head0.414
GPT teacher head0.580
Teacher spread0.165 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreProtocol

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".

Quick stats

Citations15
Published2020
Admission routes2
Has abstractyes

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