Bibliographic record
Abstract
We thank Stanton et al1 for taking time to provide feedback on our recent publication in PTJ.2 The purpose of our systematic review was to provide quality ratings for physical therapy–specific clinical prediction rule (CPR) derivation studies. It was our suspicion that CPR derivation studies reported in the physical therapy literature frequently used cohort/prognostic study designs. This suspicion was confirmed when we found that 9 out of the 10 retrieved studies used cohort/prognostic designs. Therefore, we believe our “yardstick” was consistent with our original intent. It may become necessary to implement other quality assessment criteria as physical therapy CPRs evolve to include other methods, but the current tool was appropriate for the studies included in the review.3 Stanton et al1 selected a sentence from our article to indicate that we encouraged clinical use of CPRs prior to validation. Missing from their response letter were the parts of the article in which we indicated the role of validation studies (ie, “…quality scores are not a substitute for CPR validation studies”2[p119]).Furthermore, we presented a balanced consideration of clinical application of derivation CPRs: …our findings should not be viewed as definitive. Our data provide complementary information on which CPRs to use in clinical practice, but the ultimate decision must be made in the context of a clinician's experience and factors specific to the encounter with a patient.2(p120)
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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 | 0.011 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.017 | 0.018 |
| Insufficient payload (model declined to judge) | 0.149 | 0.068 |
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".