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Record W2946261043 · doi:10.1097/bot.0000000000001470

Patient Outcomes in Orthopaedic Trauma: How to Evaluate if Your Treatment Is Really Working?

2019· review· en· W2946261043 on OpenAlexaff
Aaron Nauth, David Wasserstein, Paul Tornetta, Peter A. Cole, William T. Obremskey, Basem Attum, Gerard P. Slobogean

Bibliographic record

VenueJournal of Orthopaedic Trauma · 2019
Typereview
Languageen
FieldMedicine
TopicPelvic and Acetabular Injuries
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicinePsychosocialPsychological interventionOrthopedic surgeryIntensive care medicinePhysical therapyEvidence-based medicineMEDLINEAlternative medicineSurgeryPsychiatry

Abstract

fetched live from OpenAlex

Outcomes are critical to gauge the success of our treatments and, in particular, surgical interventions in orthopaedic trauma. Patient-reported outcomes have evolved to become the primary measurement of success in surgery. This article reviews the concepts relevant to understanding these outcomes including general health outcomes, extremity- and disease-specific outcomes, minimum clinically important difference, economic analysis of treatment cost/benefit, and the impact of psychosocial factors on outcomes. An understanding of these concepts is important to allow for effective interpretation and critical analysis of the literature as well as to facilitate the practice of evidence-based medicine.

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)0.011
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.041
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0040.005
Science and technology studies0.0010.002
Scholarly communication0.0040.005
Open science0.0020.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.082
GPT teacher head0.363
Teacher spread0.281 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations5
Published2019
Admission routes1
Has abstractyes

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