Bibliographic record
Abstract
The application of “best practices” and standardized clinical practice guidelines (CPGs) is supposed to improve health care delivery, but staying on top of developments is always a challenge. However, there is a growing number of online tools to help busy physicians apply CPGs in their practices. The CMA Infobase, mdm.ca/cpgsnew /cpgs/index.asp, is a catalogue of 1700-plus guidelines, many of which are accompanied by patient guides. A search for “breast cancer” conducted May 23 yielded 29 results, including one CPG published just a month earlier. The University of Ottawa's Health Research Institute has a tool to help doctors determine when to apply the Ottawa ankle and knee rules and CT head and cervical spine rules (www .ohri .ca/programs /clinical_epidemi o logy /OHDEC /clinical.asp). In all cases supporting research is referenced, along with links to information about the principal investigators. South of the border, the American Pain Society offers Talaria, www.talaria .org, an online implementation of the American Agency for Health Care Policy and Research CPG on pain management for cancer patients (www .ahcpr .gov). It includes a calculator for converting different dosages of opioid drugs, and there are 2 assessment tools for initial patient evaluation. For a broad collection of online decision-making tools, visit eMedicine's electronic library (www.emedicine.com/etools). It has everything from algorithms for assessing the risk of GI bleeding complications to Ranson's criteria for determining the prognosis of acute pancreatitis and the TWEAK alcoholism score. There are also calculators for estimating body mass index and converting temperatures from Celsius to Fahrenheit, and an online test for assessing acute stroke. It indicates when thrombolytic treatment is indicated and when risks of treatment outweigh the potential benefits. In all cases the clinical assessment tools are linked to the supporting research to allow physicians to judge their validity. — Michael OReilly, ten.ylliero@ekim
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.552 | 0.334 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".