Bibliographic record
Abstract
One hundred thirty cases were selected by systematic sampling from those attending the dental O. P. D. of a teaching general hospital, excluding those under 14 years and those unable to read and write Hindi well. They were administered a questionnaire carrying 50 questions covering dental and non-dental (mainly psychiatric) symptoms and the Hindi version of Middlesex Hospital Questionnaire. After this they were seen by a dental surgeon and a psychiatrist separately. Nearly a quarter of the cases had no dental disease and only slightly less than half had a defi-nite psychiatric illness. Dental O. P. D. cases scored significantly higher on the M. H. Q_. as compared to normal controls. The younger patients had the maximum score of the OBS subscale while the older ones scored maximum on the DF.P sub-scale. The number of symptoms complained of by the patients were directly related to their M. H. Q,. scores. Psychiatric aspects of dentistry have received scant attention particularly in our country. During the course of an earlier study (Shukla and Srivastava, 1981), it was felt that an appreciable pro-portion of cases attending the dental O. P. D. had no primary dental pathology. Further, many of even those with definite dental diseases had symptoms inexplicable on that account. It was therefore thought worthwhile to undertake a planned psy-chiatric study of a representative sample of dental O. P. D. cases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.568 | 0.177 |
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".