Bibliographic record
Abstract
Objectives:LuBAIR™ Paradigm, a novel approach to ascribing meaning for behavioral expression in advanced neurocognitive disorder, was used to identify behavioral categories that are likely to respond to the use of atypical antipsychotics in their management.Design:A retrospective study.Setting:St. Peter’s Hospital (SPH), Hamilton, Ontario, Canada.Participants:Forty patients qualified for the study.Intervention:LuBAIR™ Inventory populated on all recruited patients on two separate occasions. The first time was within two weeks of admission and the second time was after sixty days, if they successfully de-prescribed off the AAP, or sooner, if they failed de-prescribing.Measurements:Chi-Square paired t-test and Cohen d Statistical tests were used to detect the difference in the behavioral categories between the two cohorts.Results:Seventeen patients were successfully de-prescribed, and twenty-three failed de-prescribing. Results on the LuBAIR™ Inventory, filled on the second occasion, in the successful de-prescribed and the failed de-prescribed groups compared using the Chi-Square Statistical test to detect the difference in the behavioral categories the two cohorts. Patients who did not have Mis-Identification Expressions (MiE) and Goal-Directed Expressions (GDE), amongst the cluster of behavioral categories in their clinical presentation, were more likely to successfully de-prescribe of AAP: X2 (1, N = 40) = 29.119 p<.0001 and X2 (1, N = 40) = 32.374, p<.0001, respectively. Alternatively, the same behavioral categories were more likely to be present in patients who failed de-prescribing: paired t-test and Cohen-d (P<0.0001).Conclusion:The MIE and GDE were statistically significant, suggesting that these behaviors were more present in patients who failed de-prescribing. Atypical antipsychotics, in their role as an antipsychotic and mood stabilizer, may be used to manage Mis-Identification and Goal-Directed Expressions, respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.480 | 0.226 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".