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Record W4386650886 · doi:10.1183/20734735.0101-2023

Holistic management of patients with progressive pulmonary fibrosis

2023· review· en· W4386650886 on OpenAlexaff
Ana Oliveira, Gaia Fabbri, Thomas Gille, Elena Bargagli, Boris Duchemann, Rachael A Evans, Hilary Pinnock, Anne E. Holland, Elisabetta Renzoni, Magnus Ekström, Steve Jones, Marlies Wijsenbeek, Anh Tuan Dinh‐Xuan, Guido Vagheggini

Bibliographic record

VenueBreathe · 2023
Typereview
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPulmonary rehabilitationMedicinePsychological interventionIntensive care medicineHealth careRehabilitationIdiopathic pulmonary fibrosisNursingPhysical therapyLungInternal medicine

Abstract

fetched live from OpenAlex

Progressive pulmonary fibrosis (PF) is a complex interstitial lung disease that impacts substantially on patients' daily lives, requiring personalised and integrated care. We summarise the main needs of patients with PF and their caregivers, and suggest a supportive care approach. Individualised care, education, emotional and psychological support, specialised treatments, and better access to information and resources are necessary. Management should start at diagnosis, be tailored to the patient's needs, and consider end-of-life care. Pharmacological and non-pharmacological interventions should be individualised, including oxygen therapy and pulmonary rehabilitation, with digital healthcare utilised as appropriate. Further research is needed to address technical issues related to oxygen delivery and digital healthcare. Educational aims: To identify the main needs of patients with PF and their caregivers.To describe the components of a comprehensive approach to a supportive care programme for patients with PF.To identify further areas of research to address technical issues related to the management of patients with PF.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.780
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.042
GPT teacher head0.328
Teacher spread0.286 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations19
Published2023
Admission routes1
Has abstractyes

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