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Record W2092615715 · doi:10.1177/1357633x12473917

Utilization of Home Telemonitoring in Patients 75 Years of Age and Over with Complex Heart Failure

2013· article· en· W2092615715 on OpenAlexaffabout
Geneviève Lemay, Nahid Azad, Christine Struthers

Bibliographic record

VenueJournal of Telemedicine and Telecare · 2013
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsUniversity of Ottawa
FundersRoyal Society
KeywordsMedicineHeart failureAge groupsPsychological interventionSignificant differenceEmergency medicinePediatricsMedical emergencyInternal medicine

Abstract

fetched live from OpenAlex

We conducted a chart review on all patients who had received home telemonitoring after an admission for heart failure at the University of Ottawa Heart Institute. During a 5 year period (2005-2009) a total of 645 patients had home monitoring. A total of 594 patients met the inclusion criteria for the study and were divided into two groups: Group 1 (<75 years of age) contained 350 patients and Group 2 (≥75 years of age) contained 244 patients. There was no significant difference between the groups in the mean duration of follow-up by the telemonitoring team: it was 126.5 days in Group 1 and 125.4 days in Group 2 (P = 0.89). There were no significant differences between the groups in the number of times that titration of diuretic medications occurred, the number of times that titration of cardiac medications occurred, the number of interventions for abnormal vital signs or the number of times that patients were called by the telemonitoring staff. Emergency room visits, hospitalizations and the number of deaths were also not different between two groups. Thus in the telemonitoring programme, the pattern of usage by older patients was similar to that of the younger ones. Based on the present study, the elderly do not require more resources nor do they require them for longer.

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)0.000
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.017
GPT teacher head0.264
Teacher spread0.247 · 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
GenreEmpirical

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

Citations14
Published2013
Admission routes2
Has abstractyes

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