Evidence map›Paper›PMID 33761232›Full record

ReviewEndocrinology and metabolism (Seoul, Korea)2021

Operationalizing Treat-to-Target for Osteoporosis.

E Michael Lewiecki

Open access · diamondAbstract readReview
In one paragraph

Review in Endocrinology and metabolism (Seoul, Korea), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.2field-weighted citation impact, top 25% 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

6 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Observational
  5. Osteoporosis: Spotlight on current approaches to pharmacological treatment.Turkish journal of physical medicine and rehabilitation · 2023
    Review
  6. Article
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.

E Michael LewieckiNew Mexico Clinical Research & Osteoporosis Center, Albuquerque, NM, USA.
New Mexico Clinical Research & Osteoporosis Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Treat-to-target (TTT) for osteoporosis is a concept for individualizing patient treatment decisions that focuses on achieving an acceptable level of fracture risk rather than response to treatment alone. While a response to treatment is essential in order to achieve an acceptable level of risk, it is not necessarily sufficient. Some patients have a good response to treatment yet remain at high level of fracture risk. Since there is no way to directly measure bone strength in patients treated for osteoporosis, a surrogate measurement must be used. Bone mineral density (BMD) is commonly used to select patients for treatment and has emerged as the most useful surrogate for assessing reduction of fracture risk after treatment is started. Recent large meta-regression studies have shown a robust correlation between larger increases in BMD with treatment and greater reductions in fracture risk. Application of TTT for osteoporosis involves assessing fracture risk before starting treatment and initiating treatment with an agent that is most likely to reduce fracture risk to an acceptable level, represented by a target BMD T-score, over a reasonable period of time. This review offers suggestions for implementing TTT for osteoporosis in clinical practice and managing patients who fail or succeed in reaching the target. More study is needed to fully validate the use of TTT for osteoporosis for initiating and modifying treatments to reduce fracture risk.

Indexed as

Fractures, BoneOsteoporosisBone DensityHumansBone and bonesFractures, boneGoalsOsteoporosisTargetTherapeutics

Identifiers

PMID33761232
PMCPMC8090476
OpenAlexW3136493910

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.