Risk assessment tools to predict location of discharge and need for supportive services for medical patients after discharge from hospital: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Patients who are discharged from hospital after an acute medical illness often have impaired function that prevents them from returning to their previous place of residence. Assessing each patient's post-discharge needs takes time and resources but is important in order to reduce unplanned readmissions and adverse events post-discharge. METHODS/DESIGN: We will conduct a systematic review to synthesize the evidence on prognostic models and their reported accuracy in predicting the location of discharge after a medical admission to an acute care hospital. We will perform searches in MEDLINE, EMBASE, CINAHL, and COCHRANE databases. Pre-defined study, population, and model characteristics will be reported. We will write a narrative summary of included studies. Methodological quality of the studies will be assessed using the QUIPS tool, and the quality of evidence will be evaluated using the GRADE tool. DISCUSSION: Early and accurate assessment of patient needs for supportive services after discharge has the potential to improve patient outcomes and health system efficiency. This systematic review will identify factors that can accurately predict location of discharge using existing tools and identify priority knowledge gaps to inform future research. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42016037144.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.055 | 0.070 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.020 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".