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Record W4220867349 · doi:10.1016/j.msard.2022.103757

The agenda of the global patient reported outcomes for multiple sclerosis (PROMS) initiative: Progresses and open questions

2022· article· en· W4220867349 on OpenAlexaff
Paola Zaratin, Patrick Vermersch, Maria Pia Amato, Giampaolo Brichetto, Timothy Coetzee, Gary Cutter, Gilles Edan, Gavin Giovannoni, Emma Gray, Hans Hartung, Jeremy Hobart, Anne Helme, Robert Hyde, Usman A. Khan, Letizia Leocani, LG Mantovani, Robert McBurney, Xavier Montalbán, Iris‐Katharina Penner, Bernard M.J. Uitdehaag, Pamela Valentine, Helga Weiland, Deborah Bertorello, Mario Alberto Battaglia, Peer Baneke, Gıancarlo Comı

Bibliographic record

VenueMultiple Sclerosis and Related Disorders · 2022
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsMultiple Sclerosis Society of Canada
FundersMultiple Sclerosis International FederationMultiple Sclerosis TrustFondazione Italiana Sclerosi Multipla
KeywordsMedicinePatient-reported outcomeAgency (philosophy)Multiple sclerosisStakeholderPublic relationsQuality of life (healthcare)NursingPolitical scienceSociologyPsychiatry

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.300
metaresearch head score (Gemma)0.276
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.700
Threshold uncertainty score0.864

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3000.276
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0150.005
Bibliometrics0.0050.006
Science and technology studies0.0100.025
Scholarly communication0.0290.055
Open science0.0120.026
Research integrity0.0630.066
Insufficient payload (model declined to judge)0.0150.004

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 head0.089
GPT teacher head0.325
Teacher spread0.236 · 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 designNot applicable
DomainReporting
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".

Quick stats

Citations40
Published2022
Admission routes1
Has abstractno

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