Bibliographic record
Abstract
In his letter, Dr Melton speculates about the possible reasons for our observation on the declining rates of hip fracture in Finland since the late 1990s.(1) As we clearly pointed out in our Discussion, the exact reasons for this secular change are unknown, although many potential explanations can be given. A cohort effect toward healthier elderly population and improved functional ability among elderly Finns may indeed refer to the “compression of morbidity” in their lifespan. These phenomena are not simple and straightforward, however. In our country, evidence is strong for improved functionality among elderly women and men to the age group of 80–84,(2,3) whereas among the oldest old (persons ≥85 yr of age), the functional ability has not improved during the recent two decades.(4) The Canadian investigators, without any direct evidence for cause‐effect relationship, attributed a decline in hip fracture incidence in Ontario in 1992–2000 to increased use of bone densitometry and antiresorptive therapy.(5) In their related Editorial, Melton et al.(6) were, however, rather critical about such a straightforward interpretation. Among other criticism, Melton et al. pointed out that similarly declining hip fracture incidence rates have been seen in Rochester women since 1950 and in Rochester men since 1980, a similar climatologic region, the decline thus occurring decades before osteoporosis screenings and therapies became available. In Finland, use of bone‐specific drugs was so uncommon in the 1990s that its role must have been minor in explaining the decreased hip fracture incidence since 1997. This especially concerns our elderly men.
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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 | 0.010 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.021 | 0.023 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".