The ANAM Lacks Utility as a Diagnostic or Screening Tool for Concussion More Than 10 Days Following Injury
Bibliographic record
Abstract
Congress has mandated that the Department of Defense perform screening for concussion, or mild traumatic brain injury, on all service members redeploying from Iraq and Afghanistan. However, the retrospective diagnosis of concussion is complicated by the subjective nature of the complaints, overlap of symptoms with other conditions, and the normally rapid recovery of neurocognitive function following a concussive event. One diagnostic and screening test in current use by the Department of Defense is the Automated Neuropsychological Assessment Metrics (ANAM). A team of researchers deployed to Iraq between January and April 2009 to test the validity of the ANAM for the diagnosis of concussion in the combat environment. Performance by concussed participants on all six ANAM subtests was compared with that of controls. The ANAM appears to have no utility as an individual diagnostic or population screening tool for the detection of neurocognitive dysfunction from a single, uncomplicated concussion when administered 10 or more days following injury. Further studies are required to determine the modalities providing optimal sensitivity and specificity for use as diagnostic or screening tests beyond the first 72-hour acute postinjury period.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".