Bibliographic record
Abstract
for osteoporosis I have many middle-aged and elderly men in my family practice, including some who are being treated for osteo-porosis, and thus I read with interest the CMAJ article on managing osteo-porosis in men by Aliya Khan and col-leagues.1 I was surprised by the au-thors ’ statement that “as in the 2002 guidelines [from the Osteoporosis So-ciety of Canada], [bone mineral test-ing] for all men over 65 is advised.” The article did not provide any evi-dence to support this recommendation, so I turned to the cited guidelines.2 The first sentence in the section on osteo-porosis in men states that “there are in-sufficient data on the relation between [bone mineral density] and fracture risk in men. ” Neither the guidelines nor the article by Khan and colleagues provides information on the incidence of osteoporosis in men. It would seem to me that any further discussion should be postponed until such data are available. However, both docu-ments go on to recommend screening for men over 65 years of age. How can one propose screening for a disease when the incidence of the condition is unknown in the popula-tion in question and when use of the recommended screening tool cannot as yet be correlated with disease detec-tion? Furthermore, no evidence was provided concerning the cost of screen-ing, the number of cases of osteoporo-sis that would be diagnosed by screen-ing all men (rather than only men at high risk of developing the disease) and the number of subsequent frac-tures that would be prevented by screening.
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.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.265 | 0.095 |
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".