Factors Influencing Objective and Subjective Prospective Memory Measures in Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: To accurately assess prospective memory (PM) functioning in patients who sustained a traumatic brain injury (TBI), it is important to use both subjective (questionnaires) and objective (tests) measures. However, which factors have the most significant effect on each PM measure remains unknown. This observational study aims to verify whether TBI severity or psychological status has the most influence on patients' objective and subjective PM measures. METHOD: Fifteen healthy control (HC) participants (n = 15), 19 patients with a mild TBI (n = 19) and 30 patients with a moderate-to-severe TBI (n = 30) were recruited during the post-acute phase. For almost every participant, a relative was also recruited (n = 62). The Test écologique de mémoire prospective (TEMP), an objective computerized PM measure consisting of naturalistic stimuli, was administered to all participants along with anxiety (BAI) and depression inventories (BDI). Participants and their relatives also completed a questionnaire, the comprehensive assessment of PM (CAPM), a subjective PM measure assessing everyday failures. RESULTS: Results on the objective PM measure were predicted by TBI severity, while psychological symptoms (BAI and BDI) predicted scores on the subjective PM measure. In addition, relatives in the moderate-to-severe TBI group reported more PM failures on the subjective measure and their perception was significantly correlated with results on the objective PM measure, which was not the case for the other two groups of relatives. CONCLUSIONS: Objective PM measures are related to TBI severity and appear more robust against the influence of psychological factors than subjective PM measures.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".