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Record W3182452186 · doi:10.1136/bmjopen-2021-051821

Cross-sectional study of preprints and final journal publications from COVID-19 studies: discrepancies in results reporting and spin in interpretation

2021· article· en· W3182452186 on OpenAlexaff
Lisa Bero, Rosa Lawrence, Louis Leslie, Kellia Chiu, Sally McDonald, Matthew J. Page, Quinn Grundy, Lisa Parker, Stephanie Boughton, Jamie J Kirkham, Robin Featherstone

Bibliographic record

VenueBMJ Open · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicAcademic Publishing and Open Access
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicinePreprintCoronavirus disease 2019 (COVID-19)MEDLINEObservational studySubgroup analysisMeta-analysisPublication biasFamily medicineInternal medicineWorld Wide WebLaw

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare results reporting and the presence of spin in COVID-19 study preprints with their finalised journal publications. DESIGN: Cross-sectional study. SETTING: International medical literature. PARTICIPANTS: Preprints and final journal publications of 67 interventional and observational studies of COVID-19 treatment or prevention from the Cochrane COVID-19 Study Register published between 1 March 2020 and 30 October 2020. MAIN OUTCOME MEASURES: Study characteristics and discrepancies in (1) results reporting (number of outcomes, outcome descriptor, measure, metric, assessment time point, data reported, reported statistical significance of result, type of statistical analysis, subgroup analyses (if any), whether outcome was identified as primary or secondary) and (2) spin (reporting practices that distort the interpretation of results so they are viewed more favourably). RESULTS: Of 67 included studies, 23 (34%) had no discrepancies in results reporting between preprints and journal publications. Fifteen (22%) studies had at least one outcome that was included in the journal publication, but not the preprint; eight (12%) had at least one outcome that was reported in the preprint only. For outcomes that were reported in both preprints and journals, common discrepancies were differences in numerical values and statistical significance, additional statistical tests and subgroup analyses and longer follow-up times for outcome assessment in journal publications.At least one instance of spin occurred in both preprints and journals in 23/67 (34%) studies, the preprint only in 5 (7%), and the journal publications only in 2 (3%). Spin was removed between the preprint and journal publication in 5/67 (7%) studies; but added in 1/67 (1%) study. CONCLUSIONS: The COVID-19 preprints and their subsequent journal publications were largely similar in reporting of study characteristics, outcomes and spin. All COVID-19 studies published as preprints and journal publications should be critically evaluated for discrepancies and spin.

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)0.312
metaresearch head score (Gemma)0.646
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score0.849

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3120.646
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0150.015
Science and technology studies0.0020.003
Scholarly communication0.0060.006
Open science0.0030.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.558
GPT teacher head0.622
Teacher spread0.064 · 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 designObservational
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

Citations53
Published2021
Admission routes1
Has abstractyes

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