Evidence map›Paper›PMID 8974728›Full record

ArticleCalcified tissue international1996

The correlation of bone mineral density and biochemical markers to fracture risk.

J D Adachi

Abstract read
PubMed Publisher
In one paragraph

Article in Calcified tissue international, 1996. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
1.5field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author at 1 institution in 1 country.

J D AdachiDepartment of Medicine, McMaster University, Hamilton, Ontario, Canada.
McMaster University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bone metabolism may be measured indirectly by determining markers for bone formation and bone resorption. Traditionally, total alkaline phosphatase has served to mark bone formation, whereas urine calcium and hydroxyproline, corrected for creatinine, have marked bone resorption. Although these markers have contributed to our understanding of bone metabolism, by themselves they lack the sensitivity and specificity to be helpful in the day-to-day management of patients with osteoporosis. Newer bone markers have been developed and others are currently under development. These markers may be helpful in determining the type of osteoporosis and the response to therapy. Nonetheless, despite their increased sensitivity and specificity, they have not yet been shown to predict future fractures. Many prospective studies have shown that bone mass measured at any site and by any method is inversely related to fracture risk. Site-specific measurement of the bone density of the proximal femur appears to have a stronger relationship to the risk of hip fracture. In predicting spinal fractures, however, measurement of bone density in the lumbar spine does not appear to be substantially superior to measurements of bone density at other sites. The lifetime risk for hip fracture can be estimated from bone mass measurements made in the perimenopausal period. In the future, the combination of baseline perimenopausal bone density and assessments of biochemical markers may better predict future fracture risk; however, this has yet to be shown.

Indexed as

Bone and BonesBone DensityHumansMagnetic Resonance SpectroscopyRadiographyTomography, Emission-ComputedUltrasonography

Identifiers

PMID8974728
OpenAlexW1965221512

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.