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Record W2597972276 · doi:10.1136/bmjopen-2016-014769

Scoping review of potential quality indicators for hip fracture patient care

2017· article· en· W2597972276 on OpenAlexaff
Kristen Pitzul, Sarah Munce, Laure Perrier, Lauren A Beaupré, Suzanne N. Morin, Rhona McGlasson, Susan Jaglal

Bibliographic record

VenueBMJ Open · 2017
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsBone and Joint CanadaUniversity of AlbertaMcGill UniversityUniversity of TorontoUniversity Health NetworkToronto Rehabilitation Institute
Fundersnot available
KeywordsMedicineHip fractureAcute careHealth careQuality (philosophy)MEDLINEPeer reviewOsteoporosisPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this study is to identify existing or potential quality of care indicators (ie, current indicators as well as process and outcome measures) in the acute or postacute period, or across the continuum of care for older adults with hip fracture. DESIGN: Scoping review. SETTING: All care settings. SEARCH STRATEGY: English peer-reviewed studies published from January 2000 to January 2016 were included. Literature search strategies were developed, and the search was peer-reviewed. Two reviewers independently piloted all forms, and all articles were screened in duplicate. RESULTS: The search yielded 2729 unique articles, of which 302 articles were included (11.1%). When indicators (eg, in-hospital mortality, acute care length of stay) and potential indicators (eg, comorbidities developed in hospital, walking ability) were grouped by the outcome or process construct they were trying to measure, the most common constructs were measures of mortality (outcome), length of stay (process) and time-sensitive measures (process). There was heterogeneity in definitions within constructs between studies. There was also a paucity of indicators and potential indicators in the postacute period. CONCLUSIONS: To improve quality of care for patients with hip fracture and create a more efficient healthcare system, mechanisms for the measurement of quality of care across the entire continuum, not just during the acute period, are required. Future research should focus on decreasing the heterogeneity in definitions of quality indicators and the development and implementation of quality indicators for the postacute period.

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)0.000
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: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.100
GPT teacher head0.503
Teacher spread0.403 · 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 designSystematic review
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

Citations18
Published2017
Admission routes1
Has abstractyes

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