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Record W2619073407 · doi:10.1111/jep.12770

Examining the role of the physician as a source of variation: Are physician‐related variations necessarily unwarranted?

2017· review· en· W2619073407 on OpenAlexaff
Mathew Mercuri, Amiram Gafni

Bibliographic record

VenueJournal of Evaluation in Clinical Practice · 2017
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsHamilton Health SciencesImpactMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsVariation (astronomy)Argument (complex analysis)Context (archaeology)Narrative reviewHealth carePsychologyMedicineActuarial scienceBusinessEconomicsIntensive care medicine

Abstract

fetched live from OpenAlex

RATIONALE, AIMS, AND OBJECTIVES: The physician is often implicated as an important cause of observed variations in health care service use. However, it is not clear if physician-related variation is problematic for patient care. This paper illustrates that observed physician-related variation is not necessarily unwarranted. METHODS: This is a narrative review. RESULTS: Many studies have attributed observed variations to the physician, but little attention is given towards discriminating between those variations that exist for good reasons and those that are unwarranted. Two arguments can be made for why physician-related variation is unwarranted. The first posits that physician-related factors should not play a role in management of care decisions because such decisions should be driven by science (which is imagined to be definitive). The second considers the possibility of supplier-induced demand as a factor driving observed variations. We show that neither argument is sufficient to rule out that physician-related variations may be warranted. Furthermore, the claim that such variations are necessarily problematic for patients has yet to be substantiated empirically. CONCLUSIONS: It is not enough to simply show that physician-related variation can exist-one must also show where it is unwarranted and what is the magnitude of unwarranted variations. Failure to show this can have significant implications on how we interpret and respond to observed variations. Improved measurement of the sources of variation, especially with respect to patient preferences and context, may help us start to disentangle physician-related variation that is desirable from that which is unwarranted.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.026
metaresearch head score (Gemma)0.039
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.949
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.039
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.335
GPT teacher head0.491
Teacher spread0.156 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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

Citations26
Published2017
Admission routes1
Has abstractyes

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