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Record W2528355997 · doi:10.1186/s12913-016-1783-x

Design of an orthopaedic-specific discharge summary

2016· article· en· W2528355997 on OpenAlexaff
Christine Soong, Bochra Kurabi, Kathleen Exconde, Faiqa Tajammal, Chaim M. Bell

Bibliographic record

VenueBMC Health Services Research · 2016
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsInstitute of Health Services and Policy ResearchUniversity Health NetworkUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsMedicineNursing researchHealth informaticsHealth administrationPublic healthOrthopedic surgeryMedical physicsNursingSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Patients undergoing orthopaedic procedures experience major changes in function and daily routines upon their return home. Discharge summaries are an important communication tool that may play a role in optimizing a safe transition from hospital. Current care gaps and key elements of an ideal discharge summary specific for orthopaedic population are unknown. We sought to identify the challenges of current orthopaedic discharge summaries and to determine key elements of an ideal document. METHODS: Qualitative study survey using semi-structured interviews with a sample of 17 patients and clinicians representing diverse professions, backgrounds, and practice settings. We used the constant comparative method of qualitative analysis to define the experiences and perceptions of quality gaps and strategies to improve orthopaedic-specific discharge summaries. RESULTS: We identified 3 major themes describing factors perceived to be limiting the quality of current discharge summaries: 1) physician-centric documentation and the absence of a comprehensive, inter-professional perspective; 2) access to resources and health informatics; and 3) process variations in document creation and dissemination. CONCLUSIONS: Clinicians and patients identified several factors limiting the quality of discharge summaries among orthopaedic inpatients. Incorporating these elements could improve hospital transitions.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score0.807

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.089
GPT teacher head0.423
Teacher spread0.334 · 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

Citations11
Published2016
Admission routes1
Has abstractyes

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