A Systematic Review of Comorbidity Measurement Methods for Patients With Nontraumatic Brain Injury in Inpatient Rehabilitation Settings
Bibliographic record
Abstract
This review summarizes comorbidity measurements used on patients with nontraumatic brain injury in inpatient rehabilitation and describes findings on measurement validation and comorbidity profiles. MEDLINE and MEDLINE In-Process, EMBASE, PsycINFO, the Cochrane Database of Systematic Reviews, Health, and Psychosocial Measurement Instruments were searched. Two reviewers screened results according to predefined inclusion and exclusion criteria. Population, statistical methods, comorbidity measurement, justification of its use, and results involving comorbidity were extracted using a standard table. Of 9476 articles retrieved, 16 were included. Comorbidity has been measured using various methods including the following: number and type within various classification systems, such as the International Disease Classification system, the Charlson comorbidity index, Centers for Medicare and Medicaid Services comorbidity tiers and patient comorbidity and complexity level values and subsets of diagnoses within nonadministrative data studies. No studies have assessed the predictive ability of the comorbidity measurements for inpatient rehabilitation outcomes in this population. Because comorbidities are common among the nontraumatic brain injury population, the predictive validity of comorbidity measurements should be assessed to determine the most appropriate measure to predict or risk adjust rehabilitation outcomes, which has implications for the development of clinical guidelines, and to inform health service research, planning, and delivery.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".