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Record W2965827988 · doi:10.1080/24740527.2019.1587584

Electronic Versus Traditional Data Collection: A Multicenter Randomized Controlled Perioperative Pain Trial

2019· article· en· W2965827988 on OpenAlexaff
James S. Khan, Lindsay Jibb, Jason W. Busse, Ian Gilron, Stephen Choi, James Paul, Michael McGillion, Sean Mackey, D. Norman Buckley, Shun Fu Lee, P.J. Devereaux

Bibliographic record

VenueCanadian Journal of Pain · 2019
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsPopulation Health Research InstituteSunnybrook Health Science CentreQueen's UniversityMcMaster UniversityImpactChildren's Hospital of Eastern OntarioUniversity of OttawaUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsElectronic data captureMedicineElectronic dataData collectionRandomized controlled trialPerioperativeClinical trialPhysical therapyData entryRandomizationSurgeryInternal medicineDatabaseComputer science

Abstract

fetched live from OpenAlex

: Electronic data collection is feasible for perioperative pain clinical trials. Additional trials, including different surgical populations, are needed to confirm our findings and optimize use of electronic data capture methods.

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)0.012
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.265
Teacher spread0.234 · 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 designRandomized trial
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

Citations11
Published2019
Admission routes1
Has abstractyes

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