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Record W3095617969 · doi:10.2519/jospt.2020.0109

Overcoming Overuse Part 2: Defining and Quantifying Health Care Overuse for Musculoskeletal Conditions

2020· article· en· W3095617969 on OpenAlexaff
Zoe A Michaleff, Joshua R Zadro, Adrian C. Traeger, Mary O’Keeffe, Simon Décary

Bibliographic record

VenueJournal of Orthopaedic and Sports Physical Therapy · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMedicineHealth carePhysical therapyNursing

Abstract

fetched live from OpenAlex

Summary In this series on “Overcoming Overuse,” we explore the issue of health care overuse and how it may be identified in musculoskeletal physical therapy. In part 2, we frame health care overuse as a continuum from overuse to appropriate care, and consider how to measure overuse. We describe how overuse can be defined within a framework of care that is ineffective, inefficient, and misaligned, depending on the perspective of the person delivering or receiving care—the clinician, society, or patient. To ensure that musculoskeletal health care is of high value and sustainable, we encourage physical therapists to reflect on their practice. J Orthop Sports Phys Ther 2020;50(11):588–591. doi:10.2519/jospt.2020.0109

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.807

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.397
GPT teacher head0.519
Teacher spread0.122 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations14
Published2020
Admission routes1
Has abstractyes

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