Quality of life in patients with multiple injuries -- basic issues, assessment, and recommendations
Bibliographic record
Abstract
BACKGROUND: While the primary goal of trauma care continues to be the preservation of life, interest has begun to focus on disability and quality of life of those who survive. Numerous instruments have been developed to measure personal well-being, impairment, or subjective life-satisfaction. But there is no consensus regarding which instruments are most appropriate to use in multiply injured patients, and comparison of results are difficult. OBJECTIVE: The objective of this multinational conference was to arrive at a consensus regarding the measurement of quality of life in survivors of multiple trauma. Specifically we sought to identify the best time intervals for measurement and a minimum set of instruments. METHOD: The group reviewed instruments currently in use for quality of life measurement in multiply injured patients. A structured discussion covered the following topics: definition of the population, the concept of quality of life, the importance of different domains of quality of life at different time points, the type of measures and their validity, consistency, and practicability, the mode of administration, subject burden, and availability of population norms. RESULTS: The group suggested three time points, after 3, 12 and 24 months, for the assessment of quality of life after multiple injury. The Glasgow Outcome Scale (GOS) was suggested as an overall global outcome measure including death and vegetative state. The EuroQol was proposed to permit economic analysis, and the SF-36 as a validated global quality of life measure. CONCLUSION: While most selected measures are psychometrically sound, many have had limited use in the setting of multiple injuries. Researchers and clinicians may use these suggestions as a source of information when developing a measurement strategy.
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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| 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".