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Record W3152935742 · doi:10.1101/2021.04.12.21255329

Comparison of preprints and final journal publications from COVID-19 Studies: Discrepancies in results reporting and spin in interpretation

2021· preprint· en· W3152935742 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

VenuemedRxiv · 2021
Typepreprint
Languageen
FieldDecision Sciences
TopicAcademic Publishing and Open Access
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Observational studyStatistical significanceMedicineSubgroup analysisOutcome (game theory)MEDLINEPreprintMeta-analysisPublication biasStatisticsFamily medicineInternal medicineMathematicsComputer science

Abstract

fetched live from OpenAlex

ABSTRACT Objective To compare results reporting and the presence of spin in COVID-19 study preprints with their finalized journal publications Design Cross-sectional 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 March 1, 2020 and October 30, 2020 Main outcome measures Study characteristics and discrepancies in 1) Results reporting (number of outcomes, outcome descriptor, measure (e.g., PCR test), metric (e.g., mean change from baseline), assessment time point (e.g., 1 week post treatment), data reported (e.g., effect estimate and measures of precision), reported statistical significance of result, type of statistical analysis (e.g., chi-squared test), subgroup analyses (if any), whether outcome was identified as primary or secondary and 2) Spin (reporting practices that distort the interpretation of results so that results are viewed more favorably). 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; 8 (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 conducted in journal publications, 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%) studies, and the journal publications only in 2 (3%) of studies. 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. EQUATOR REPORTING GUIDELINE STROBE What is already known on this topic Selective and incomplete reporting of results and spin are threats to the trustworthiness and validity of research. These reporting practices could be particularly dangerous for users of COVID-19 research as they can inflate the efficacy of interventions and underestimate harms. Given the high prevalence, visibility, and potentially rapid implementation of COVID-19 research published as preprints, it is important to compare components of results reporting and the presence of spin in COVID-19 studies on treatment or prevention that are published both as preprints and journal publications. What this study adds This comparison of 67 COVID-19 preprints related to treatment or prevention and their subsequent journal publications found they were largely similar in reporting of study characteristics, components of results reporting and spin in interpretation. Even a few important discrepancies could impact decision making.

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)0.022
metaresearch head score (Gemma)0.302
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.302
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.442
GPT teacher head0.559
Teacher spread0.116 · 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 teacher head, not a consensus.

Study designObservational
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".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

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