CONECT-6: a case-finding tool to identify patients with complex health needs
Bibliographic record
Abstract
Abstract Background Early identification of patients with chronic conditions and complex health needs in emergency departments (ED) would enable the provision of services better suited to their needs, such as case management. A case-finding tool would ultimately support ED teams to this end and could reduce the cost of services due to avoidable ED visits and hospitalizations. The aim of this study was to develop and validate a short self-administered case-finding tool in EDs to identify patients with chronic conditions and complex health needs in an adult population. Methods This prospective development and initial validation study of a case-finding tool was conducted in four EDs in the province of Quebec (Canada). Adult patients with chronic conditions were approached at their third or more visit to the ED within 12 months to complete a self-administered questionnaire, which included socio-demographics, a comorbidity index, the reference standard INTERMED self-assessment, and 12 questions to develop the case-finding tool. Significant variables in bivariate analysis were included in a multivariate logistic regression analysis and a backward elimination procedure was applied. A receiver operating characteristic (ROC) curve was developed to identify the most appropriate threshold score to identify patients with complex health needs. Results Two hundred ninety patients participated in the study. The multivariate analysis yielded a six-question tool, COmplex NEeds Case-finding Tool – 6 (CONECT-6), which evaluates the following variables: low perceived health; limitations due to pain; unmet needs; high self-perceived complexity; low income; and poor social support. With a threshold of two or more positive answers, the sensitivity was 90% and specificity 66%. The positive and negative predictive values were 49 and 95% respectively. Conclusions The case-finding process is the essential characteristic of case management effectiveness. This study presents the first case-finding tool to identify adult patients with chronic conditions and complex health needs in ED.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".