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Record W2893820555 · doi:10.1001/jamasurg.2018.3222

Frequency of Misinterpretation of Inconclusive Noninferiority Trials

2018· article· en· W2893820555 on OpenAlexafffund
Sergio A. Acuña, Fahima Dossa, Nancy N. Baxter

Bibliographic record

VenueJAMA Surgery · 2018
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Surgical Treatments
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsMedicineClinical trialGeneral surgeryMEDLINESurgeryInternal medicine

Abstract

fetched live from OpenAlex

owing to a higher rate of readmission. Limitations to our study include limited information regarding long-term follow-up, cost, and stratification based on institution type or hospital volume. There are also constraints inherent in the database we used such as potential variability in the interpretation of patient data by those who compile the database. These findings emphasize that proper discharge planning and medical support after discharge are of utmost importance.

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)0.503
metaresearch head score (Gemma)0.865
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.497
Threshold uncertainty score0.613

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5030.865
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0130.007
Science and technology studies0.0020.005
Scholarly communication0.0080.007
Open science0.0040.004
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0160.003

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 head0.063
GPT teacher head0.366
Teacher spread0.302 · 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
DomainMethods
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

Citations18
Published2018
Admission routes2
Has abstractyes

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