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Record W2943697963 · doi:10.1111/ctr.13580

Outcomes of telehealth care for lung transplant recipients

2019· article· en· W2943697963 on OpenAlexaffabout
Aman Sidhu, Cecilia Chaparro, Chung‐Wai Chow, M. Davies, L.G. Singer

Bibliographic record

VenueClinical Transplantation · 2019
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsTelehealthMedicineTelemedicineCohortCohort studyEmergency medicineLung transplantationRetrospective cohort studyPhysical therapyTransplantationInternal medicineHealth care

Abstract

fetched live from OpenAlex

Telehealth uses videoconferencing to provide long-distance clinical care. Experience with telehealth in the setting of organ transplantation is limited. The purpose of this cohort study was to compare the impact of telehealth vs in-person follow-up of lung transplant recipients. Telehealth eligible patients were three or more years post-transplant and resided in Ontario outside the Greater Toronto Area. Patients with initial telehealth visits between July 1, 2009, and Dec 31, 2014, were retrospectively reviewed to assess outcomes of chronic lung allograft dysfunction progression and mortality until December 31, 2016, compared with eligible patients seen in-person. Of eligible patients (n = 204), 119 (58.3%) were seen via telehealth. Most patients (97%) rated telehealth as equivalent or superior to clinic visits. Telehealth visits resulted in significant out-of-pocket cost savings and travel distance savings for patients. There was no significant difference in mortality from the time of first visit (HR 0.81, 95% CI 0.49-1.32, P = 0.4) or from the time of transplant between groups (HR 0.72, 95% CI 0.43-1.17, P = 0.2). Telehealth can safely and effectively be used in select transplant recipients to increase access to care and reduce time and financial burdens for patients residing greater distances from primary transplant centers.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.009
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.062
GPT teacher head0.451
Teacher spread0.389 · 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 teacher head, 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

Citations25
Published2019
Admission routes2
Has abstractyes

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