Design of an orthopaedic-specific discharge summary
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".