Evidence map›Paper›PMID 23689044›Full record

ArticleMedical decision making : an international journal of the Society for Medical Decision Making2013

Patient and societal value functions for the testing morbidities index.

J Shannon Swan, Chung Yin Kong, Janie M Lee, Omosalewa Itauma, Elkan F Halpern, Pablo A Lee, Sergey Vavinskiy, Olubunmi Williams, Emilie S Zoltick, Karen Donelan

Abstract read
In one paragraph

Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

10 authors.

J Shannon SwanMassachusetts General Hospital Institute for Technology Assessment, Boston, MA (JSS, CYK, JML, OA, EFH, PL, OW, ESZ, KD)
Chung Yin KongMassachusetts General Hospital Institute for Technology Assessment, Boston, MA (JSS, CYK, JML, OA, EFH, PL, OW, ESZ, KD)
Janie M LeeMassachusetts General Hospital Institute for Technology Assessment, Boston, MA (JSS, CYK, JML, OA, EFH, PL, OW, ESZ, KD)
Omosalewa ItaumaMassachusetts General Hospital Institute for Technology Assessment, Boston, MA (JSS, CYK, JML, OA, EFH, PL, OW, ESZ, KD)
Elkan F HalpernMassachusetts General Hospital Institute for Technology Assessment, Boston, MA (JSS, CYK, JML, OA, EFH, PL, OW, ESZ, KD)
Pablo A LeeMassachusetts General Hospital Institute for Technology Assessment, Boston, MA (JSS, CYK, JML, OA, EFH, PL, OW, ESZ, KD)
Sergey VavinskiyIndiana University Department of Radiology, Indianapolis, IN (SV)
Olubunmi WilliamsMassachusetts General Hospital Institute for Technology Assessment, Boston, MA (JSS, CYK, JML, OA, EFH, PL, OW, ESZ, KD)
Emilie S ZoltickMassachusetts General Hospital Institute for Technology Assessment, Boston, MA (JSS, CYK, JML, OA, EFH, PL, OW, ESZ, KD)
Karen DonelanMassachusetts General Hospital Institute for Technology Assessment, Boston, MA (JSS, CYK, JML, OA, EFH, PL, OW, ESZ, KD)

Funding

Clinical and Economic Evaluation of Breast MRI for High-Risk ScreeningK07CA128816 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI LEE, JANIE M · 2008 to 2012
$713k
NCI NIH HHS K07 CA128816
6 · The paper itself

Abstract

backgroundWe developed preference-based and summated scale scoring for the Testing Morbidities Index (TMI) classification, which addresses short-term effects on quality of life from diagnostic testing before, during, and after testing procedures.

methodsThe two TMI preference functions use multiattribute value techniques; one is patient-based and the other has a societal perspective, informed by 206 breast biopsy patients and 466 (societal) subjects. Because of a lack of standard short-term methods for this application, we used the visual analog scale (VAS). Waiting tradeoff (WTO) tolls provided an additional option for linear transformation of the TMI. We randomized participants to 1 of 3 surveys: The first derived weights for generic testing morbidity attributes and levels of severity with the VAS; a second developed VAS values and WTO tolls for linear transformation of the TMI to a "dead-healthy" scale; the third addressed initial validation in a specific test (breast biopsy). The initial validation included 188 patients and 425 community subjects. Direct VAS and WTO values were compared with the TMI. Alternative TMI scoring as a nonpreference summated scale was included, given evidence of construct and content validity.

resultsThe patient model can use an additive function, whereas the societal model is multiplicative. Direct VAS and the VAS-scaled TMI were correlated across modeling groups (r = 0.45-0.62). Agreement was comparable to the value function validation of the Health Utilities Index 2. Mean absolute difference (MAD) calculations showed a range of 0.07-0.10 in patients and 0.11-0.17 in subjects. MAD for direct WTO tolls compared with the WTO-scaled TMI varied closely around 1 quality-adjusted life day.

conclusionsThe TMI shows initial promise in measuring short-term testing-related health states.

Indexed as

MorbidityPatientsBreast NeoplasmsFemaleHumansPsychometricsoutcomes researchpreventive medicinepublic healthscreening

Identifiers

PMID23689044
PMCPMC3817009

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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.