Appendix: International commentaries
Bibliographic record
Abstract
The editors summarized the contributions written by colleagues in different parts of the world (Chapter 6) to illustrate the similarities, and occasional differences, in the management of depression in older people described in all the contributions. This appendix allows the reader to read the individual contributions. Australia Assessment This 82-year-old woman is chronically disabled by pain and breathlessness and appears to have become socially disengaged. She has several symptoms of depression, including persistent low mood, loss of energy (which sounds to be out of proportion to her medical state), early morning waking, loss of interest in previously enjoyed activities, and persistent feelings that life is not worth living. The vignette does not provide information about her appetite and weight, concentration, any psychomotor changes, guilt feelings or confidence levels, but even so it is clear that, provided the symptoms have been present for two weeks (and this seems highly likely), she meets both DSM-IV diagnostic criteria for a major depressive episode and ICD-10 criteria for a depressive episode. Australian health-care system Within the Australian health-care system, in which specialists are accessible only after referral from a general practitioner (GP), this woman would normally be managed by her GP who in all likelihood will already be engaged in the management of her troublesome osteoarthritis and chronic obstructive pulmonary disease (COPD). She might well attend a respiratory outpatient clinic or rheumatology clinic in a public hospital, or (less likely as fewer than one-third of the elderly have private health insurance) be seeing a private medical specialist with expertise in one or both of these two areas.
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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 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.471 | 0.244 |
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".