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Record W2038969580 · doi:10.1186/scrt179

Cell therapy demonstrates promise for acute respiratory distress syndrome - but which cell is best?

2013· letter· en· W2038969580 on OpenAlexaff
Gerard F. Curley, John G. Laffey

Bibliographic record

VenueStem Cell Research & Therapy · 2013
Typeletter
Languageen
FieldMedicine
TopicMesenchymal stem cell research
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsARDSMedicineMesenchymal stem cellCell therapyStem-cell therapyStem cellIntensive care medicineStromal cellImmunologyLungPathologyInternal medicineBiology

Abstract

fetched live from OpenAlex

Acute respiratory distress syndrome (ARDS) constitutes a spectrum of increasingly severe acute respiratory failure and is the leading cause of death and disability in the critically ill. There are no therapies for ARDS, and management remains supportive. Cell therapy, particularly with allogeneic mesenchymal stem/stromal cells (MSCs), has emerged as a promising therapeutic strategy for ARDS, favorably modulating the immune response to reduce lung injury, while facilitating lung regeneration and repair. In this issue of the journal, Rojas and colleagues provide us with a rationale to consider autologous bone marrow-mononuclear cells as an alternative to MSCs for this devastating disease.

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)0.002
metaresearch head score (Gemma)0.006
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.017
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0170.021
Insufficient payload (model declined to judge)0.0050.004

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 head0.121
GPT teacher head0.379
Teacher spread0.258 · 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
GenreCommentary

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

Citations6
Published2013
Admission routes1
Has abstractyes

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