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Record W3175305832 · doi:10.1002/hon.111_2881

PRIORITISATION OF RELEVANT COCHRANE REVIEW TOPICS IN THE FIELD OF HAEMATOLOGY

2021· article· en· W3175305832 on OpenAlexaboutno aff
Carolina Domingues Hirsch, Thilo Jakob, E. Tomlinson, Lise J Estcourt, Sebastian Theurich, Sunday Ocheni, Nicole Skoetz, Vanessa Piechotta

Bibliographic record

VenueHematological Oncology · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHematologySystematic reviewInternal medicineCochrane LibraryMEDLINEFamily medicineMeta-analysisMedical educationPolitical science

Abstract

fetched live from OpenAlex

Background: Cochrane Haematology produces, publishes and maintains systematic reviews in the field of haematology. The group wants to ensure that their limited resources and efforts are being used to produce Cochrane reviews addressing topics that are of utmost importance to their end-users. According to one of Cochrane's key principle ‘striving for relevance' the group initialised their first priority setting exercise engaging stakeholders worldwide to identify the top priority topics for Cochrane Haematology reviews. The aim was to identify the top ten priority topics for review updates and the top five priority topics for new Cochrane Haematology reviews as part of Cochrane Haematology's priority setting exercise. Methods: The priority setting exercise consisted of two phases. In phase one, potential priority topics were generated by identifying trends (analysing review metrics) and gaps (screening for priority topics of the American Society of Hematology (ASH), the European Hematology Association (EHA), the German Society for Hematology and Oncology (DGHO) and the James Lind Alliance (JLA) in the field of hematology) in the current review portfolio. In the second phase, the identified topics were then prioritised by various stakeholders in an online survey. Respondents were asked to select the topics they deemed to be of high priority and rank them afterwards from highest to lowest priority. An average score was then calculated for each ranked topic. Topics with the highest average score were identified as being the top priority topics. Results: The online survey was open between July 6th 2020 to August 28th 2020. A total of 160 responses were collected, of which 63 respondents (39%) provided complete responses. Most of the respondents identified themselves as physician (34%), someone who is or has been affected by haematological disease (31%), or researcher (24%) and were distributed across 21 countries. The highest number of respondents resided in the United Kingdom (n = 72), Germany (n = 20) and Canada (n = 11). The top ten priority topics for reviews that need updating were related to interventions in multiple myeloma, hodgkin lymphoma and topics from the areas of stem cell transplantation and supportive care for haematological diseases. The top five priority topics for new reviews addressed mainly myelodysplastic syndrome (MDS), relapse of disease post-haematopoietic stem cell transplant and long-term survival in cancer. Conclusion: The priority setting process engaged various stakeholders and identified the most relevant review topics, published on Cochrane Haematology's website. These topics, some of them are open to new authors, will now guide the group's future scope of work and ensure that the most important reviews are updated. New priority reviews will close gaps within the review portfolio of Cochrane Haematology. EA – previously submitted to regional or national meetings (up to 1000 attendees) and EHA 2021. Keywords: Cancer Health Disparities, Lymphoid Cancers - Other No conflicts of interests pertinent to the abstract.

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)0.037
metaresearch head score (Gemma)0.126
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.602
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0370.126
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.611
GPT teacher head0.582
Teacher spread0.029 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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