MétaCan
Menu
Back to cohort
Record W4402008342 · doi:10.1183/13993003.01180-2024

Management of pulmonary hypertension in special conditions

2024· review· en· W4402008342 on OpenAlexaff
Ioana R. Preston, Luke Howard, David Langleben, Mona Lichtblau, Tomás Pulido, Rogério Souza, Karen M. Olsson

Bibliographic record

VenueEuropean Respiratory Journal · 2024
Typereview
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsMedicineTask forceTask (project management)Pulmonary hypertensionExpert opinionSocioeconomic statusSpecial Interest GroupIntensive care medicineHealth carePerioperativeEnvironmental healthCardiologySurgeryManagement

Abstract

fetched live from OpenAlex

Care of pulmonary hypertension (PH) patients in special situations requires insightful knowledge of the pathophysiology of the cardiopulmonary system and close interaction with different specialists, depending on the situation. The role of this task force was to gather knowledge about five conditions that PH patients may be faced with. These conditions are 1) perioperative care; 2) management of pregnancy; 3) medication adherence; 4) palliative care; and 5) the influence of climate on PH. Many of these aspects have not been covered by previous World Symposia on Pulmonary Hypertension. All of the above conditions are highly affected by psychological, geographical and socioeconomic factors, and share the need for adequate healthcare provision. The task force identified significant gaps in information and research in these areas. The current recommendations are based on detailed literature search and expert opinion. The task force calls for further studies and research to better understand and address the special circumstances that PH patients may encounter.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.107
GPT teacher head0.370
Teacher spread0.263 · 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 designNot applicable
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

Citations39
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Respiratory JournalSame topicPulmonary Hypertension Research and TreatmentsFrench-language works237,207