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Measuring Patient-Reported Outcomes: Key Metrics in Reconstructive Surgery

2018· review· en· W2739679870 on OpenAlex
Sophocles H. Voineskos, Jonas A. Nelson, Anne F. Klassen, Andrea L. Pusic

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnnual Review of Medicine · 2018
Typereview
Languageen
FieldMedicine
TopicBreast Implant and Reconstruction
Canadian institutionsMcMaster University
FundersNational Cancer InstituteNational Institutes of Health
KeywordsPromPatient-reported outcomeRasch modelPsychosocialQuality of life (healthcare)Reliability (semiconductor)PsychometricsMedicineMedical physicsPsychologyPhysical therapyComputer scienceClinical psychologyNursingPsychiatryDevelopmental psychology

Abstract

fetched live from OpenAlex

Satisfaction and improved quality of life are among the most important outcomes for patients undergoing plastic and reconstructive surgery for a variety of diseases and conditions. Patient-reported outcome measures (PROMs) are essential tools for evaluating the benefits of newly developed surgical techniques. Modern PROMs are being developed with new psychometric approaches, such as Rasch Measurement Theory, and their measurement properties (validity, reliability, responsiveness) are rigorously tested. These advances have resulted in the availability of PROMs that provide clinically meaningful data and effectively measure functional as well as psychosocial outcomes. This article guides the reader through the steps of creating a PROM and highlights the potential research and clinical uses of such instruments. Limitations of PROMs and anticipated future directions in this field are discussed.

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.

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)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.817
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0080.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.102
GPT teacher head0.353
Teacher spread0.251 · 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