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Record W4407554448 · doi:10.1016/j.amjsurg.2025.116250

Inconsistency between abstract and main text reporting is common in pilot randomized controlled trials of surgical interventions: A methodological survey

2025· review· en· W4407554448 on OpenAlexaff
Tyler McKechnie, Tania Kazi, Victoria Shi, Austine Wang, Sophia Zhang, Alex Thabane, Keean Nanji, Phillip Staibano, Lily Park, Aristithes Doumouras, Cagla Eskicioglu, Lehana Thabane, Sameer Parpia, Mohit Bhandari

Bibliographic record

VenueThe American Journal of Surgery · 2025
Typereview
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsSt. Joseph’s Healthcare HamiltonImpactMcMaster University
Fundersnot available
KeywordsMedicineRandomized controlled trialConfidence intervalPsychological interventionMEDLINEClinical trialIntervention (counseling)Family medicineSurgeryInternal medicinePsychiatry

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Reporting · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptMeta-epidemiology (broad)Metaresearch
Domain: Reporting · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models splitAgreement compares identical category sets and study designs across arms.

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)0.593
metaresearch head score (Gemma)0.868
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: Reporting
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.407
Threshold uncertainty score0.501

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5930.868
Meta-epidemiology (narrow)0.0020.004
Meta-epidemiology (broad)0.0100.028
Bibliometrics0.0150.012
Science and technology studies0.0020.007
Scholarly communication0.0090.007
Open science0.0040.007
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0060.001

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 head0.944
GPT teacher head0.658
Teacher spread0.287 · 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

Labeled directly by 2 models reading the full record.

MetaresearchMeta-epidemiology (broad)

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

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

Citations2
Published2025
Admission routes1
Has abstractno

Explore more

Same venueThe American Journal of SurgerySame topicMeta-analysis and systematic reviewsCategoryMetaresearchFrench-language works237,207