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Record W4401390307 · doi:10.2106/jbjs.24.00400

Is the PASS the New Gold Standard for Outcome Measures?

2024· article· en· W4401390307 on OpenAlexaff
Johnny Lau

Bibliographic record

VenueJournal of Bone and Joint Surgery · 2024
Typearticle
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicinePatient satisfactionGold standard (test)Physical therapyPatient-Reported Outcomes Measurement Information SystemPatient-reported outcomeComputerized adaptive testingOutcome (game theory)MEDLINEQuality of life (healthcare)Physical medicine and rehabilitationSurgeryPsychometricsClinical psychologyNursing

Abstract

fetched live from OpenAlex

Commentary The PROMIS (Patient-Reported Outcomes Measurement Information System) is a widely used outcome measure that is administered using computerized adaptive testing. As patient-reported outcome measures (PROMs) evolve, clinicians and investigators need to consider whether statistically significant changes in them are clinically important for patients. This paper helps to determine whether changes in the PROMIS outcomes after total ankle replacement (TAR) are clinically important by identifying the patient acceptable symptom state, or PASS. The PASS is the symptom threshold beyond which patients consider themselves well. Patient satisfaction is not achieved simply by obtaining a certain level of pain relief or functional improvement, and it can also vary depending on patient expectations as well as the diagnosis and procedure. Surgeons should therefore aim to ensure that patients have realistic expectations for TAR outcomes. This study highlights the strong association between postoperative PROMIS pain scores and the ability to achieve the PASS, showing the importance of pain reduction in patient satisfaction. Furthermore, this study identifies several preoperative variables that may affect achievement of the PASS. Patients with better preoperative physical function and mental health scores were more likely to achieve the PASS for physical function postoperatively. Patients with prior surgery, diabetes, or peripheral vascular disease were less likely to achieve the PASS for physical function. However, the PASS value can vary depending on the anchor question. There is no gold standard to capture patient satisfaction, and anchor questions with different wording might result in different thresholds. An anchor question focused on pain will not be relevant to a patient concerned about function. Thus, multiple anchors may be used to evaluate the PASS. The anchor questions used in this study specifically asked about satisfaction with the surgery and about the acceptability of current foot and ankle symptoms and function. However, the PROMs that they used were the PROMIS Physical Function (V1.2), Pain Interference (V1.1), Pain Intensity (3a, V1.0), Global Physical Health, Global Mental Health, and Depression (V1.0). None of these PROMs are foot and ankle-specific. In Figure 1, the graph shows very little change, particularly for Global Physical Health, Global Mental Health, and Depression, from before to after surgery1. So, what is the relevance of satisfaction with surgery and of foot and ankle symptoms for these outcomes? Would foot and ankle-specific PROMs result in different conclusions? The authors of this study have shown the importance of considering patient satisfaction, using PROMIS scores, and setting realistic expectations after TAR. The PASS thresholds for TAR are poorer than the population norm, so patients do not need to reach normal pain or physical function levels to have an acceptable symptom state after surgery. The overall PASS achievement rate was 84%. Thus, 1 in 6 patients did not achieve symptoms and function after TAR, similar to the rates in studies of hip and knee arthroplasty.

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)0.208
metaresearch head score (Gemma)0.576
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.208
Threshold uncertainty score0.976

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2080.576
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0060.005
Science and technology studies0.0020.016
Scholarly communication0.0080.012
Open science0.0130.004
Research integrity0.0160.027
Insufficient payload (model declined to judge)0.0040.003

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 head0.090
GPT teacher head0.312
Teacher spread0.223 · 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.

Study designObservational
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

Citations0
Published2024
Admission routes1
Has abstractyes

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