Research on Patients with Multiple Health Conditions: Different Constructs, Different Views, One Voice
Bibliographic record
Abstract
Abstract: Technological advances, improvements in medical care and public health policies have resulted in a growing proportion of patients with multiple health conditions. The prevalence of multiple health conditions among individuals increases with age, is substantial among older adults, and will increase dramatically in coming years [1–4]. This phenomenon has received growing interest in the most recent literature and has led to several – and often differing – conceptualizations.\n\nThe term “comorbidity” was originally defined by Feinstein as “any distinct additional clinical entity that has existed or may occur during the clinical course of a patient who has the index disease under study” [5]. This definition places one disease in a central position and all other condition(s) as secondary, in that they may or may not affect the course and treatment of the index disease [6]. Feinstein’s principle has been applied all too readily as if the effect of comorbidity was secondary or indeed negligible. In clinical research, individuals with a narrowly defined index condition and no major comorbidities are usually enrolled, leaving the majority of the patients seen in a typical family practice [7,8] out in the cold. In clinical practice, management of the index condition invariably takes priority, with disjointed – if any – treatment plans developed for each of the comorbidities [6]. This model of care is typical of delivery systems constructed around specialized care, where areas of expertise are defined around specific conditions and bodily systems [11]. Not surprisingly, clinical practice guidelines arising from that model of care lack pertinence for patients with multiple health conditions [9,10].
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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 | 0.104 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.008 | 0.044 |
| Scholarly communication | 0.019 | 0.034 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 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".