Modern approaches to the assessment of comorbidity in patients
Bibliographic record
Abstract
Aim. To provide modern data on advantages and disadvantages of available international comorbidity scales and indices. Materials and methods. Data of 29 scientific sources published in Russian and foreign literature press within 1973-2018 are considered. Results. The presence of comorbidity in a patient is an issue of modern medicine. In most cases some comorbid diseases if timely diagnosed and managed in accordance with algorithms for medical care can be corrected and treated. In order to control risks of development of complications and to prescribe an effective therapy for comorbidity the international and national clinical guidelines have been created. They include algorithms for clinical and instrumental assessment of complications and provide scales and indices, such as Cumulative lllness Rating Scale (CIRS), Charlson comorbidity index, Kaplan-Feinstein index, Index of Co-Existent Disease (ICED), Geriatric Index of Comorbidity (GIC), Functional Comorbidity Index (FCI) et al. Data of Canadian comparative study of 5 international scales of comorbidity in patients with head and neck cancers showed a significant impact of comorbidity on survival of patients with different stages of neoplasms. It was emphasized that the index of comorbidity is necessary to control an impact of comorbid diseases on the patients' status in the long-term period. The Kaplan-Feinstein scale was the best index for assessing a survival of patients with head and neck cancer. According to V.de Groot, the most widely studied comorbidity index for predicting mortality is the Charlson index. Each index has its advantages and disadvantages and is used in different clinical situations. Conclusion. General comorbidity index is a comprehensive summary score of a disease combination or severity, which combines all conditions, problems and illnesses of patients, weights them by severity, and it significantly affects treatment tactics and outcome in a future.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".