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Record W2045053681 · doi:10.1111/jch.12284

JNC 8 or Too Much of a Good Thing

2014· article· en· W2045053681 on OpenAlexaboutno aff
Norman M. Kaplan

Bibliographic record

VenueJournal of Clinical Hypertension · 2014
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePaymentReading (process)Family medicineBlood pressureOrder (exchange)Hypertension treatmentInternal medicineFinanceLawPolitical science

Abstract

fetched live from OpenAlex

The claim that “too much of a good thing is wonderful” has been attributed to Mae West. Her observation, however, does not apply to guidelines for the treatment of hypertension. In the past few weeks, new guidelines have been proposed by at least four groups: members of the Eighth Joint National Committee (JNC 8),1 the American Society of Hypertension/International Society of Hypertension (ASH/ISH),2 the European Society of Hypertension/European Society of Cardiology (ESH/ESC),3 and the American Heart Association (AHA).4 These come on top of new guidelines from Canada5 and the United Kingdom.6 Despite spending a goodly amount of time in reading the literature about hypertension, I may have missed a few. With much less time and interest, what are busy practitioners (the audience to whom the guidelines are addressed) to do? Very likely they will change very little from their current management of hypertension, at least until someone requires a certain level of blood pressure to be reached in their hypertensive patients in order to receive payment from various third-party payers. Medical practices logically may differ in different places at different times. However, those experts who compose guidelines are aware of the same data upon which guidelines are based. Why then do guidelines differ? Some differences are based on the availability of new data, as between JNC 7 and JNC 8. But the major reason for differences between the four guidelines proposed over the same time period of the past few weeks likely reflects the willingness to accept evidence from observational data or meta-analyses that include relatively small trials of limited duration by all but JNC 8.1 The JNC 8 guidelines accept data only from large randomized controlled trials and when such data are not available from “expert opinion.” Not surprisingly, JNC 8 backs away from the inadequate support for lower goals for patients with diabetes or chronic kidney disease and those older than 60 years. Even though the editor of the second most cited journal of hypertension is heartened by the similarities in the ASH/ISH and ESH/ESC guidelines,7 the different levels of blood pressure wherein drug therapy is required and the different goals of therapy between JNC 8 and the other three is a major difference. Meanwhile, advocacy has been given to the concept that treatment be based not on levels of blood pressure, ie, “treatment to target,” but rather on overall cardiovascular risk, ie, “benefit-based tailored regimen,” although the assessment of risk does include a level of blood pressure.8 The “benefit-based tailored regimen” is claimed to provide greater protection from cardiovascular diseases with less medication. The approach suggested by Sussman and colleagues8 is in keeping with the recently published new guidelines for treatment of hypercholesterolemia,9 which have been both praised as being “a brave and wise departure from current practice”10 but also said to result in both undertreatment for some and overtreatment for others.11 For the foreseeable future, treatment to target will likely be continued. But beyond that issue, some greater uniformity over the two major issues—when to start drug treatment and what is the goal of therapy to be reached—is essential if both patients and practitioners are to be convinced of the best way to treat hypertension.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.139
Threshold uncertainty score0.465

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.044
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.003
Scholarly communication0.0090.004
Open science0.0030.004
Research integrity0.0110.010
Insufficient payload (model declined to judge)0.1390.115

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.

Opus teacher head0.487
GPT teacher head0.560
Teacher spread0.073 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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