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Record W3175593668 · doi:10.1101/2021.06.18.21259151

A Delphi study to establish a consensus definition and clinical reporting guidelines for Mesenchymal Stromal Cells

2021· preprint· en· W3175593668 on OpenAlexaffabout
Laurent Renesme, Kelly D. Cobey, Maxime Lê, Manoj M. Lalu, Bernard Thébaud

Bibliographic record

VenuemedRxiv · 2021
Typepreprint
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsUniversity of OttawaChildren's Hospital of Eastern OntarioOttawa Hospital
Fundersnot available
KeywordsDelphi methodLikert scaleDelphiResearch ethicsTranslational researchInclusion (mineral)Medical educationPsychologyMedicinePublic relationsKnowledge managementPolitical scienceComputer sciencePathologySocial psychology

Abstract

fetched live from OpenAlex

ABSTRACT Introduction Despite being more than two decades of research, Mesenchymal Stromal Cell (MSC) treatments are still struggling to cross the translational gap. Two key issues that likely contribute to these failures are i) the lack of clear definition for MSC and ii) poor quality of reporting in MSC clinical studies. To address these issues, we propose a modified Delphi study to establish a consensus definition for MSC and clinical reporting guidelines for MSC. Methods and analysis We will conduct a three-round international modified Delphi Survey. Findings from a recent scoping review examining how MSC are defined and reported in preclinical and clinical studies were used to draft the initial survey for round one of our Delphi. Participants will include a ‘core group’ of individuals as well as researchers whose work was captured in our scoping review. The core group will include stakeholders from different research fields including developmental biology, translational science, research methods, regulatory practices, scholarly journal editing, and industry. The first two survey rounds will be online, and the final round will take place in person. Each participant will be asked to rate their agreement on potential MSC definition characteristics and reporting items using a Likert scale. After each round, we will analyse data to determine which items have reached consensus for inclusion/exclusion, and then develop a revised questionnaire for any new items, or items that did not reach consensus. Ethics and dissemination This study received ethical approval from the Ottawa Health Research Network Research Ethics Board. To support the dissemination of our findings, we will use an evidence-based ‘integrated knowledge translation’ approach to engage knowledge users from the inception of the research. This will allow us to develop a tailored end-of-project knowledge translation plan to support and ensure dissemination and implementation of the Delphi results. Strengths and limitations of this study We proposed to address the current limitations in MSC experimental and clinical research with a rigorous and methodological consensus building method (Delphi method) that will allow for structured communication on controversial issues. To support dissemination and implementation of our results, we will engage stakeholders and end-users from the inception of the project – such as patient partners – and will develop a tailored end of project knowledge translation plan (integrated knowledge translation approach) in order to overcome historical issues related to community uptake. To address the main limitations of a Delphi method (e.g., lack of participation, no in-person interaction or information exchange), we use a modified Delphi survey with a Core group of stakeholders and a face-to-face meeting.

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)0.376
metaresearch head score (Gemma)0.387
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.624
Threshold uncertainty score0.770

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3760.387
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0070.004
Science and technology studies0.0080.008
Scholarly communication0.0060.012
Open science0.0040.016
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0090.002

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 head0.567
GPT teacher head0.576
Teacher spread0.009 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designQualitative
DomainReporting
GenreMethods

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

Citations0
Published2021
Admission routes2
Has abstractyes

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