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Outcomes‐Based Patient Care in Veterinary Surgery: What Is An Outcome Measure?

2007· article· en· W2056330842 on OpenAlexaboutno aff
James L. Cook

Bibliographic record

VenueVeterinary Surgery · 2007
Typearticle
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMeasure (data warehouse)Outcome (game theory)Patient careVeterinary medicineNursingData mining

Abstract

fetched live from OpenAlex

WHAT TECHNIQUE for stabilizing cruciate-deficient stifles provides the most functional outcome? Does total hip replacement improve quality of life more significantly than excision arthroplasty? Is arthroscopy superior to arthrotomy for ameliorating clinical signs associated with elbow dysplasia? These are the types of clinically relevant questions that need to be asked as the first step in an outcomes-based approach to patient care. How do we answer them and how do we use the answers for clinical decision making? Most surgeons would agree that our ultimate goal in clinical practice is to restore quality of life for our patients and clients. To accomplish this goal, surgeons must make decisions regarding application of diagnostic and therapeutic modalities. An outcomes-based medicine approach to decision making simplifies this process and gives surgeons more confidence in delivery of care. In 2006, a group of Diplomates interested in improving the quality of clinical studies in veterinary surgery initiated the Outcomes Measures Program (OMP) for small animal orthopedics. Our overall goal is to advance patient care by providing and supporting mechanisms for all surgeons to perform and use the highest quality clinical trials for evaluation of safety and efficacy of diagnostic and therapeutic modalities. In a series of short peer-reviewed articles, we hope to address the mechanisms, value, purpose, applicability, feasibility, and relevance of outcomes-based medicine and the OMP in clinical practice. We hope that these articles will stimulate discussion and interest in what we believe is a critical endeavor for all members of our colleges. The purpose of this initial article is to define outcomes measures and instruments, and provide an overview of their potential roles in patient care. Outcomes-based medicine can be defined as the conscientious, explicit, and judicious use of the current best evidence in making decisions for individual cases.1–3 Therefore, this a patient-oriented approach where the evaluation of outcomes from all available sources is integrated to derive the optimal decision for a given case. It is critical to understand that the foundation for this approach is the individual clinician making decisions for the individual patient. The purpose of an outcomes-based approach is to provide outcomes instruments that are standardized, reliable, and validated so that a clinician can add clinically relevant evidence to his or her knowledge and experience in making optimal decisions for each patient with confidence. It is critical that clinicians, researchers, and clients understand that all evidence is important for accomplishing the goals of outcomes-based medicine. Systematic reviews, prospective clinical trials, controlled laboratory studies, in vitro experiments, case series and reports, advice from mentors, personal experience, extrapolation, and even intuition can all be part of the best current evidence available for optimal clinical decision making. However, not all evidence is equal with respect to applicability, relevance, impact, and power for optimal decision making for our patients. It is the way in which we use and communicate the evidence that makes a difference in our delivery of care.4 For example, recommending use of a surgical procedure for a patient based on your experience in your practice and communicating that to the client is using the best current evidence when published studies using fair and direct comparisons are not available. However, suggesting that a diagnostic or therapeutic approach is superior to another without that higher level of evidence is not valid. Methods for evaluating the quality of evidence and steps for clinically implementing an outcomes-based approach are reported in the literature2 and will be the subject of subsequent articles in this series. The bottom line is that a balanced application of published data, clinical evidence, and individual experience will optimize patient care. Quality outcomes instruments play an integral part in providing quality evidence for the clinician in this approach. A major problem facing veterinary surgeons is the paucity of valid and reliable clinical evidence for optimal decision making. To make “the best” decision for our patients and clients, surgeons need to have a method for performing valid assessments and fair and direct comparisons. We would not simply accept that a sports team will be league champion, a new car gets the best gas mileage, or a personal digital assistant stores the most data without a fair comparison to others, would we? Why would we accept that then for our patients? We need to have fair comparisons for diagnostic and therapeutic interventions for our patients. A method for making fair and direct comparisons among diagnostic tests and interventions in a valid (measuring what we think we are measuring) and reliable (measuring in a consistent and repeatable manner) is an appropriate outcome measure. When the same valid and reliable outcome measures are used in different studies and centers, fair and direct comparisons among tests and interventions can be made. An outcomes instrument is a specific tool for providing data that measure a specific outcome, such as joint reaction forces from a force-plate, responses on a questionnaire, or direct measurements of thigh girth. Ideally, outcomes instruments are objective and quantitative to avoid bias and maximize the integrity of the data. However, there are limitations in our ability to use these types of outcomes instruments in every study, and importantly, the resultant outcome measures may not effectively address the defined and clinically relevant question being studied. For example, whereas force-plate derived kinetic data are accurate, objective, and quantitative, this outcomes instrument is not readily accessible by all veterinary surgeons, does not remove all forms of bias, and currently is not completely standardized among centers. Outcome measures should be standardized so that everyone is using the same system for comparison among centers, studies, techniques, and time points. Owner assessments and veterinarian evaluations are other important outcomes instruments that can be used to address clinically relevant questions.5 However, they need to be standardized so that fair comparisons can be made. Most importantly, all outcomes instruments need to be valid and reliable so that we can determine the quality, power, impact, and relevance of the data produced for use in patient-oriented decision making. For veterinary orthopedics, development of standardized client questionnaires and clinical assessment forms for function and quality of life that are validated to kinetic, kinematic, imaging, and other objective measures of outcome would seem to be the most logical approach for addressing the current shortcomings in study design and implementation for application of outcomes-based medicine in our field. This is a primary objective for the OMP and will be the subject of the next article in this series. There are good examples of validated outcomes instruments in human orthopedics. The short form 36 (SF-36), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Knee injury and Osteoarthritis Outcome Score (KOOS), and International Knee Documentation Committee (IKDC) Subjective Knee Form have all been validated in some way to provide information regarding the accuracy, reproducibility, and relevance of the data produced.6–9 Importantly, use of these standardized and validated outcomes instruments have provided direct and accurate answers to clinically relevant questions in human orthopedics similar to those presented at the beginning of this article. Whereas this may seem difficult, complicated, and labor intensive, it actually provides an easier system for study design, comparative evaluation of presentations and publications, client communication and patient care. What questionnaire or form should we use when designing a study evaluating long-term outcomes of treatment for femoral angular limb deformities? Are the data retrieved from the most recent publication on shoulder instability valid, accurate, and applicable? How do NSAIDs compare with nutriceuticals for management of elbow OA in dogs? What technique should I use to stabilize a spinal fracture in a cat and what should I tell the owner regarding postoperative physical therapy and prognosis? Outcomes-based medicine provides the tools and methodology to answer these questions for us. It requires a philosophical commitment by clinicians, researchers, and organized veterinary medicine; time and effort toward developing, validating, and using outcomes instruments that address the relevant clinical questions; and financial support. The next steps include providing further background information for veterinary surgeons, engaging veterinary surgeons in this effort to use outcomes-based medicine, incorporation of this approach in resident training, and leadership from journals and organized veterinary surgery to promote outcomes-based surgery. Subsequent articles in this series will outline these initial steps. Outcomes-based medicine is the practice of conscientious and judicious integration of individual clinical expertise with the best available evidence in making decisions about the optimal care of individual patients.1 This approach is vital for ethical, efficient, and effective patient care and client education. Optimal patient care and client communication will result in improved surgeon satisfaction by reducing client confusion, increasing client compliance, decreasing complications, and improving outcomes. It is vital that we educate ourselves and train our residents to understand and use this methodology to optimize confidence in our clinical decision making for patients and clients, and to progress in veterinary surgery. The author acknowledges the important contributions of Drs. John Pascoe, Kurt Schulz, Dottie Brown, Amy Kapatkin, Bob Taylor, Steve Budsberg, and John Innes to this OMP and preparation of this manuscript.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.088
GPT teacher head0.334
Teacher spread0.246 · 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 designObservational
Domainnot available
GenreEmpirical

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".

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Citations24
Published2007
Admission routes1
Has abstractyes

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