The eSMAF: a software for the assessment and follow-up of functional autonomy in geriatrics
Bibliographic record
Abstract
BACKGROUND: Functional status or disability forms the core of most assessment instruments used to identify mix and level of resources and services needed by older adults who possess common characteristics. The Functional Autonomy Measurement System (SMAF) is a 29-item scale measuring functional ability in five different areas. It has been recommended for use for home care, for allocation of chronic beds, for developing care plans in institutional settings and for epidemiological and evaluative studies. The SMAF can also be used with a case-mix classification system (Iso-SMAF) to allocate resources based on patients' functional autonomy characteristics. The objective of this project was to develop a software version of the SMAF to facilitate the evaluation of the functional status of older adults in health services research and to optimize the clinical decision-making process. RESULTS: The eSMAF was developed over an 24-month period using a modified waterfall software engineering process. Requirements and functional specifications were determined using focus groups of stakeholders. Different versions of the software were iteratively field-tested in clinical and research environments and software adaptations made accordingly. User documentation and online help were created to assist the deployment of the software. The software is available in French or English versions under a 30-day unregistered demonstration license or a free restricted registered academic license. It can be used locally on a Windows-based PC or over a network to input SMAF data into a database, search and aggregate client data according to clinical and/or administrative criteria, and generate summary or detailed reports of selected data sets for print or export to another database. CONCLUSION: In the last year, the software has been successfully deployed in the clinical workflow of different institutions in research and clinical applications. The software performed relatively well in terms of stability and performance. Barriers to implementation included antiquated computer hardware, low computer literacy and access to IT support. Key factors for the deployment of the software included standardization of the workflow, user training and support.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".