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Randomized Clinical Trials in Orthopaedic Surgery: Strategies to Improve Quantity and Quality

2010· article· en· W2228165692 on OpenAlexaff
S. Samuel Bederman, Josie Chundamala, James G. Wright

Bibliographic record

VenueJournal of the American Academy of Orthopaedic Surgeons · 2010
Typearticle
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineBlindingRandomized controlled trialClinical trialQuality (philosophy)Sample size determinationIntensive care medicineQuality of evidencePhysical therapyMEDLINEMedical physicsSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Randomized clinical trials (RCTs) generally provide the highest quality and least biased evidence for treatment effectiveness. Relatively few high-quality RCTs have been published in the orthopaedic literature. Barriers to increasing the quantity of trials include the orthopaedic culture, patient preferences, and the availability of treatment outside trials. Challenges to conducting better quality trials include sample size, random allocation, and blinding. Undertaking more high-quality trials can improve the evidence available for determining treatment effectiveness, resulting in better patient care.

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)0.856
metaresearch head score (Gemma)0.944
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.144
Threshold uncertainty score0.177

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.8560.944
Meta-epidemiology (narrow)0.0090.007
Meta-epidemiology (broad)0.0270.009
Bibliometrics0.0320.029
Science and technology studies0.0050.024
Scholarly communication0.0220.034
Open science0.0130.023
Research integrity0.0170.021
Insufficient payload (model declined to judge)0.0150.004

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 head0.094
GPT teacher head0.438
Teacher spread0.344 · 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 designTheoretical or conceptual
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

Citations22
Published2010
Admission routes1
Has abstractyes

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