An evaluation of the comparative effectiveness of geriatrician-led comprehensive geriatric assessment for improving patient and healthcare system outcomes for older adults: a protocol for a systematic review and network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Comprehensive geriatric assessment (CGA) is an integrated model of care involving a geriatrician and an interdisciplinary team and can prioritize and manage complex health needs of older adults with multimorbidity. CGAs differ across healthcare settings, ranging from shared care conducted in primary care settings to specialized inpatient units in acute care. Models of care involving geriatricians vary across healthcare settings, and it is unclear which CGA model is most effective. Our objective is to conduct a systematic review and network meta-analysis (NMA) to examine the comparative effectiveness of various geriatrician-led CGAs and to identify which models improve patient and healthcare system level outcomes. METHODS: An integrated knowledge translation approach will be used and knowledge users (KUs) including patients, caregivers, geriatricians, and healthcare policymakers will be involved throughout the review. Electronic databases including MEDLINE, EMBASE, Cochrane library, and Ageline will be searched from inception to November 2016 to identify relevant studies. Randomized controlled trials of older adults (≥65 years of age) that examine geriatrician-led CGAs compared to any intervention will be included. Primary and secondary outcomes will be selected by KUs to ensure the results are relevant to their decision-making. Two reviewers will independently screen the search results, extract data, and assess risk of bias. Data will be synthesized using an NMA to allow for multiple comparisons using direct (head-to-head) as well as indirect evidence. Interventions will be ranked according to their effectiveness using surface under the cumulative ranking curve (SUCRA). DISCUSSION: As the proportion of older adults grows worldwide, the demand for specialized geriatric services that help manage complex health needs of older adults with multimorbidity will increase in many countries. Results from this systematic review and NMA will enhance decision-making and the efficient allocation of scarce geriatric resources. Moreover, active involvement of KUs throughout the review process will ensure the results are relevant to different levels of decision-making. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42014014008.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.043 | 0.007 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".