Normative data for the modified balance error scoring system in adults
Bibliographic record
Abstract
PRIMARY OBJECTIVE: Head trauma, with or without injury to the brain, can impair balance and postural stability. The Modified Balance Error Scoring System (M-BESS) is a rapid, standardized, objective bedside test that can be helpful for monitoring recovery of balance and postural stability following head trauma. The purpose of this study is to develop preliminary normative data for this test for adults. METHODS AND PROCEDURES: Adults between the ages of 20-69 (n = 1234) were administered the M-BESS as part of a comprehensive preventive health screen. They did not have significant medical, neurological or lower extremity problems that might have an adverse effect on balance. MAIN OUTCOMES AND RESULTS: M-BESS performance significantly declined with age. Men and women performed similarly on the M-BESS. There was a small significant difference in M-BESS performance, with obese men performing more poorly than non-obese men and a larger significant difference between obese and non-obese women. CONCLUSIONS: The M-BESS normative data are presented for the total sample and by age, sex and age-by-sex. These normative data provide a frame of reference for interpreting M-BESS performance in adults who sustain traumatic brain injuries and adults with diverse neurological problems.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".