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Record W4375845044 · doi:10.1186/s40635-023-00510-w

Research priorities for therapeutic plasma exchange in critically ill patients

2023· review· en· W4375845044 on OpenAlexaff
Sascha David, Lene Russell, Pedro Castro, Andry Van de Louw, Lara Zafrani, Tasneem Pirani, Nathan D. Nielsen, Éric Mariotte, Bruno L. Ferreyro, Jan T. Kielstein, Luca Montini, Anne Brignier, Matthias Kochanek, Joan Cid, Chiara Robba, Ignacio Martín‐Loeches, Marlies Ostermann, Nicole P. Juffermans

Bibliographic record

VenueIntensive Care Medicine Experimental · 2023
Typereview
Languageen
FieldImmunology and Microbiology
TopicComplement system in diseases
Canadian institutionsSinai Health SystemUniversity Health Network
Fundersnot available
KeywordsMedicineCritically illTherapeutic plasma exchangeIntensive care medicineCritical illnessInternal medicine

Abstract

fetched live from OpenAlex

Therapeutic plasma exchange (TPE) is a therapeutic intervention that separates plasma from blood cells to remove pathological factors or to replenish deficient factors. The use of TPE is increasing over the last decades. However, despite a good theoretical rationale and biological plausibility for TPE as a therapy for numerous diseases or syndromes associated with critical illness, TPE in the intensive care unit (ICU) setting has not been studied extensively. A group of eighteen experts around the globe from different clinical backgrounds used a modified Delphi method to phrase key research questions related to "TPE in the critically ill patient". These questions focused on: (1) the pathophysiological role of the removal and replacement process, (2) optimal timing of treatment, (3) dosing and treatment regimes, (4) risk-benefit assumptions and (5) novel indications in need of exploration. For all five topics, the current understanding as well as gaps in knowledge and future directions were assessed. The content should stimulate future research in the field and novel clinical applications.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.460
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.197
GPT teacher head0.473
Teacher spread0.276 · 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 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

Citations30
Published2023
Admission routes1
Has abstractyes

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