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Record W3168109150 · doi:10.2106/jbjs.21.00030

A Call for a Standardized Approach to Reporting Patient-Reported Outcome Measures

2021· article· en· W3168109150 on OpenAlexaff

Bibliographic record

VenueJournal of Bone and Joint Surgery · 2021
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPromMinimal clinically important differencePatient-reported outcomeOutcome (game theory)MEDLINEData collectionCohortRelevance (law)

Abstract

fetched live from OpenAlex

ABSTRACT: A shift toward a value-driven health-care model has made prospective collection of patient-reported outcome measures (PROMs) inextricably tied to measuring the success of orthopaedic surgery and patient satisfaction. While progress has been made in optimizing the utilization of PROM data, including establishing appropriate PROMs for a procedure and determining the clinical importance of unique tools, if these PROMs are not accurately analyzed and reported, a proportion of patients who do not reach the clinical thresholds may go unnoticed. Furthermore, parameters are unclear for setting a statistically and clinically important PROM threshold along with a minimum period for follow-up data collection.In this forum, we walk through simulated data sets modeling PROMs with the example of total joint arthroplasty. We discuss how the commonly used method of reporting PROMs by mean change can overestimate the treatment effects for the cohort as a whole and fail to capture distinct populations that are below a clinically relevant threshold. We demonstrate that when a study's outcome is PROMs, clinical importance should be reported using clinical thresholds such as the minimum clinically important difference (MCID), the smallest change in the treatment outcome that a patient perceives as beneficial, and the patient acceptable symptom state (PASS), the highest level of symptoms beyond which a patient considers himself or herself well. Finally, we propose a standardized reporting of PROMs that incorporates both the MCID and the PASS, and introduce a "clinical relevance ratio," which relies on a clinically relevant threshold to dichotomize outcomes and reports the number of patients achieving clinical importance at a given time point divided by the total number of patients included in the study. Unlike other common PROM-reporting approaches, the clinical relevance ratio is not skewed by patients who are lost to follow-up with increased time.

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)0.719
metaresearch head score (Gemma)0.785
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.281
Threshold uncertainty score0.346

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.7190.785
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0050.006
Bibliometrics0.0110.011
Science and technology studies0.0030.014
Scholarly communication0.0150.017
Open science0.0100.010
Research integrity0.0080.027
Insufficient payload (model declined to judge)0.0020.002

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 head0.307
Teacher spread0.205 · 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
DomainReporting
GenreCommentary

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

Citations91
Published2021
Admission routes1
Has abstractyes

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