Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Clinicians often wonder if the single sentence from the Folstein Mini-Mental Status Exam (MMSE) offers meaningful information about the patient. We compared single sentences derived from the MMSE generated by 3 groups of participants — hospitalized medically-ill patients with psychiatric comorbidity, hospitalized medically-ill patients without psychiatric comorbidity, and non-hospitalized non-psychiatric participants. These sentences were analyzed for themes using manual thematic coding and a semi-automatic computerized method, the Meaning Extraction Method (MEM). We found that thematic content obtained from as little as a single sentence could differentiate between participant groups using both methods. Specifically, psychiatric patients used more power themes, focused on states other than the present, and were less interpersonally engaged than the other groups. Thematic content also indicated cognitive status through scores on the Clock Drawing Test (CDT) and MMSE. Our findings suggest that a single sentence can provide meaningful information about patients with medical and psychiatric comorbidity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it