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Record W2099563950 · doi:10.1186/1477-7525-10-23

Discussing study limitations in reports of biomedical studies- the need for more transparency

2012· editorial· en· W2099563950 on OpenAlexaff
Milo A. Puhan, Elie A. Akl, Dianne Bryant, Feng Xie, Giovanni Apolone, Gerben ter Riet

Bibliographic record

VenueHealth and Quality of Life Outcomes · 2012
Typeeditorial
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsMcMaster UniversityWestern University
Fundersnot available
KeywordsPublicationTransparency (behavior)HonestyEngineering ethicsPublishingIncentiveInterpretation (philosophy)PsychologyQuality (philosophy)EpistemologyPublic relationsComputer sciencePolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Unbiased and frank discussion of study limitations by authors represents a crucial part of the scientific discourse and progress. In today's culture of publishing many authors or scientific teams probably balance 'utter honesty' when discussing limitations of their research with the risk of being unable to publish their work. Currently, too few papers in the medical literature frankly discuss how limitations could have affected the study findings and interpretations. The goals of this commentary are to review how limitations are currently acknowledged in the medical literature, to discuss the implications of limitations in biomedical studies, and to make suggestions as to how to openly discuss limitations for scientists submitting their papers to journals. This commentary was developed through discussion and logical arguments by the authors who are doing research in the area of hedging (use of language to express uncertainty) and who have extensive experience as authors and editors of biomedical papers. We strongly encourage authors to report on all potentially important limitations that may have affected the quality and interpretation of the evidence being presented. This will not only benefit science but also offers incentives for authors: If not all important limitations are acknowledged readers and reviewers of scientific articles may perceive that the authors were unaware of them. Authors should take advantage of their content knowledge and familiarity with the study to prevent misinterpretations of the limitations by reviewers and readers. Articles discussing limitations help shape the future research agenda and are likely to be cited because they have informed the design and conduct of future studies. Instead of perceiving acknowledgment of limitations negatively, authors, reviewers and editors should recognize the potential of a frank and unbiased discussion of study limitations that should not jeopardize acceptance of manuscripts.

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)0.827
metaresearch head score (Gemma)0.934
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: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.173
Threshold uncertainty score0.213

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.8270.934
Meta-epidemiology (narrow)0.0030.007
Meta-epidemiology (broad)0.0100.008
Bibliometrics0.0150.011
Science and technology studies0.0170.076
Scholarly communication0.0560.076
Open science0.0170.029
Research integrity0.0480.065
Insufficient payload (model declined to judge)0.0040.003

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 head0.918
GPT teacher head0.660
Teacher spread0.257 · 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
GenreEditorial

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

Citations52
Published2012
Admission routes1
Has abstractyes

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