Medical knowledge decline: the role of active usage
Bibliographic record
Abstract
This paper details the reason for the decline in medical knowledge after initial certification of physician assistants/associates (PAs) and suggests improvement in competency assessment after initial certification. We hypothesized that the decline was caused by less frequency of use; in other words, knowledge retention was impacted by the active use of knowledge. If so, the likelihood of a decline in knowledge is mediated by the closeness of the test content to the practitioners' daily practice. Data from Physician Assistants (PA) initial certification (PANCE) and re-certification (PANRE-LA, after 6 years) were used for the current study. To quantify the level of active usage, knowledge subdomains were classified into three categories for each medical specialty: dominant, relevant and distant, ranging from the most frequently used to the least used knowledge, which was verified by four independent board-certified PAs with clinical and educational experience. To test the hypothesis, Latent transition analysis (LTA) is used to measure the probability of transitions among behavioral patterns over time, in particular how various levels of transition probabilities (e.g., probability from proficient switching to non-proficient) are related to the frequency of use. We found that the trends of knowledge decline are influenced by practice profile (medical specialty), mainly, knowledge active in daily use (i.e., dominant knowledge) over time- the less frequent the knowledge is used, the more likely the knowledge decline will take place. In particular, compared to dominant knowledge (i.e., most frequently used knowledge), relevant knowledge (i.e., mediumly frequent used knowledge) and distant knowledge (i.e., rarely used knowledge) are more likely to decline (OR = 2.31, CI = [1.82, 2.94], p < 0.001; OR = 2.26, CI = [1.84, 2.78], p < 0.001). Moreover, dominant system knowledge has a better chance to improve over the years as compared to relevant and distant system knowledge (OR = 2.19, CI = [1.71, 2.81], p < 0.001; OR = 2.12, CI = [1.72, 2.65], p < 0.001). Instead of a uniform knowledge decay, medical practitioners suffer from a differential likelihood of knowledge decay over different systems knowledge. Implications for re-certification exams are discussed.
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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 | 0.006 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".