Predicting the need for supportive services after discharged from hospital: a systematic review
Bibliographic record
Abstract
BACKGROUND: Some patients admitted to acute care hospital require supportive services after discharge. The objective of our review was to identify models and variables that predict the need for supportive services after discharge from acute care hospital. METHODS: We performed a systematic review searching the MEDLINE, CINAHL, EMBASE, and COCHRANE databases from inception to May 1st 2017. We selected studies that derived and validated a prediction model for the need for supportive services after hospital discharge for patients admitted non-electively to a medical ward. We extracted cohort characteristics, model characteristics and variables screened and included in final predictive models. Risk of bias was assessed using the Quality in Prognostic Studies tool. RESULTS: Our search identified 3362 unique references. Full text review identified 6 models. Models had good discrimination in derivation (c-statistics > 0.75) and validation (c-statistics > 0.70) cohorts. There was high quality evidence that age, impaired physical function, disabilities in performing activities of daily living, absence of an informal care giver and frailty predict the need for supportive services after discharge. Stroke was the only unique diagnosis with at least moderate evidence of an independent effect on the outcome. No models were externally validated, and all were at moderate or higher risk of bias. CONCLUSIONS: Deficits in physical function and activities of daily living, age, absence of an informal care giver and frailty have the strongest evidence as determinants of the need for support services after hospital discharge. TRIAL REGISTRATION: This review was registered with PROSPERO #CRD42016037144.
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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 | 0.013 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".