High rate of errors in pillbox filling by cognitively healthy elderly people
Bibliographic record
Abstract
An appropriate medication management depends on executive system integrity, which can be affected by aging. Previous studies showed that seniors commit frequent errors when having to fill in a pillbox. Nevertheless, to the best of our knowledge, no study has really considered the absence of cognitive disorders in the studied sample. The present study aimed to investigate pillbox filling in cognitively healthy elderly (specially focusing on executive system preservation) for whom no cognitive deterioration neither any depressive episode had occurred during a one year period. The filling task has been completed using a weekly pillbox and eight fictitious drugs. The selection of the 27 seniors aged from 71 to 90 years has been based on their results to neuropsychological tests (Trail making test, Stroop Victoria, Tower of London, Montreal cognitive assessment) and a depression assessment scale (Short geriatric depression scale). Results showed that 67% of the participants committed at least one error when filling the pillbox and 56% at least 3. The maximal number of errors was 38. Further, the errors analysis showed that 85% of the errors had been repeated (e.g. reproduced on several days). Finally, the more complex the drug prescriptions are, the higher the error rate is. No other variable (age, gender, level education, habit of filling a pillbox) had any effect on the number of errors. So, the pillbox filing task can be considered as a complex task associated with a high risk of errors. Moreover, the absence of cognitive disorders is not a success factor to the task. A prospect for the future should be to try to limit the error rate by developing, for instance, an external support helping to the filling of the pillbox and a learning process for the use of this support.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".