037 Updating an Adapted CPG: When Is Enough Enough?
Bibliographic record
Abstract
Background Within 2 years of releasing a low back pain clinical practice guideline (CPG), the Alberta Ambassador Guideline Adaptation Programme was required to update its adapted guideline. No guidance or ‘how to’ manuals were located. Objectives To develop a process for updating an adapted guideline. To expedite the process by determining which components can be removed without compromising rigour. Methods CPGs and systematic reviews published since the release of the CPG were identified and appraised, and discordant and new recommendations were tabulated. The Guideline Development Group (GDG) was surveyed to identify new interventions of interest. Evidence from systematic reviews was included for ‘do not know’ recommendations and new interventions. Results The original guideline had 50 recommendations, eight of which were in the ‘do not know’ category. This expanded to 85 recommendations in the update: 43 unchanged, 32 on GDG-nominated new interventions, and 10 revised. The updated CPG has 33 ‘do not know’ recommendations. One of the original eight ‘do not know’ recommendations was changed based on new evidence. Discussion The challenge of maintaining the integrity and high standards of the original guideline meant that the update consumed more time and resources than planned. Clearly, some components of the process can be jettisoned without jeopardising the methodological rigour and comprehensiveness of the final product. Implications The next update will be streamlined, including only new seed guidelines that meet the quality criteria of the modified AGREE and using systematic reviews to supplement the evidence base when there is discordance among recommendations.
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 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 | 0.130 | 0.451 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".