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Record W2767412346

Rebuttal of Con: Should Evidence-Based Medicine Be Used More in Clinical Practice?

2006· article· en· W2767412346 on OpenAlexaboutno aff
Chris Fee

Bibliographic record

VenueeScholarship (California Digital Library) · 2006
Typearticle
Languageen
FieldMedicine
TopicAppendicitis Diagnosis and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRebuttalEmergency departmentRandomized controlled trialClinical trialSports medicineGeneral surgeryEmergency medicineFamily medicineInternal medicinePhysical therapyPsychiatry
DOInot available

Abstract

fetched live from OpenAlex

The California Journal of Emergency Medicine VII:1, Jan-Mar, 2006 Page 19 REFERENCES 1. Wolfe JM et al. Does morphine change the physical examination in patients with acute appendicitis? American Journal of Emergency Medicine 2. Thomas SH et al. Effects of morphine analgesia on diagnostic accuracy in emergency department patients with abdominal pain: a prospective randomized trial. Journal of American College of Surgeons 3. LoVecchio F et al. The use of analgesic in patients with acute abdominal pain. Journal of Emergency Medicine 1997;15:775-779. 4. Vernculen B et al. Acute appendicitis: influence of early pain relief on the accuracy of clinical and US findings in the decision to operate—a randomized trial. Radiology 1999;210:639-643. 5. Evidence-Based Medicine Working Group, “Evidence-based medicine: a new approach to teaching the practice of medicine. JAMA 1992;268:2420-2425. 6. Haydel M et al. Indication for computed tomography in patients with minor head injury. New England Journal of Medicine 2000;343:100-105. 7. Stiell IG et al. The Canadian CT head rule for patients with minor head injury. Lancet 2001;357:1391- Rebuttal of Con Chris Fee, MD After reading both opening pieces, I am struck more by the similarities in our attitudes toward increasing the utilization of EBM in clinical practice than our differences. We do differ in our opinions of the utility of clinical experience and common sense (“plausible theorizing”). There are innumerable examples of how dangerous this approach to medicine can be. One could, through common sense and pathophysiologic knowledge, conclude that chest pain that does not resolve with nitroglycerine but subsides with administration of Maalox cannot be cardiac, but is likely to have a gastrointestinal etiology. Many of our differences can be explained by failing to acknowledge the complete definition of EBM. Recall the full definition: “the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients.” 1 This does not imply that every medical decision must be supported by a RCT or meta-analysis. Clearly, not all clinical issues are significant enough to warrant a RCT. Many decisions necessitate the use of “current best” available evidence. If a clinical scenario exists that occurs frequently, presents sufficient risk to patients, and has no clear best approach, perhaps a study should be conducted. All it takes is a clinician/ researcher with the interest, time, training, and resources. Other clinical questions will never be examined by a RCT due to ethical concerns, consent issues, or rarity of the event/illness. Thus, many of our patients do receive care based upon “soft” evidence (the “best available” evidence). But do we truly know that “soft” evidence improves care of our patients, as my colleague states? The beauty of EBM is its dynamic nature and ability to evolve and incorporate new data as it becomes available. As we amass more information with time, we will have fewer clinical quandaries and less reliance upon “soft” evidence. Every emergency physician understands the importance of throughput. However, this should not supercede providing appropriate care. EBM is rife with decision rules aimed at meeting both of these goals: the Ottawa foot, ankle, and knee rules, the Nexus and Canadian C-spine rules, Wells criteria for pre-test probability of deep venous thrombosis, and the Pneumonia Severity Index score to name a few. Correctly applying these rules may safely increase throughput by avoiding unnecessary tests and admissions. My colleague unintentionally highlights another tremendously important component of EBM: one must know how to read, interpret, critique, and apply the literature. Is the study’s data internally consistent? Were the groups truly randomized? Were the statistical tools correctly applied and performed? Are the conclusions appropriate? These questions evaluate a study’s internal validity. The generalizability (or external validity) of a study must be assessed with

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)0.017
metaresearch head score (Gemma)0.171
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: Commentary
Teacher disagreement score0.044
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.171
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.003
Science and technology studies0.0050.011
Scholarly communication0.0130.013
Open science0.0070.006
Research integrity0.0440.061
Insufficient payload (model declined to judge)0.0360.039

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 head0.089
GPT teacher head0.349
Teacher spread0.261 · 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".

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Citations0
Published2006
Admission routes1
Has abstractyes

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