Predicting the outcome of conservative treatment with physiotherapy in adults with shoulder pain associated with partial-thickness rotator cuff tears – a prognostic model development study
Bibliographic record
Abstract
Rotator cuff disorders represent the commonest type of painful shoulder complaints in clinical practice. Although conservative treatment including physiotherapy is generally recommended as first-line treatment, little is known about the precise treatment indications for subgroups of rotator cuff disorders, particularly people with shoulder pain associated with partial-thickness tears of the rotator cuff, PTTs: “symptomatic PPTs”. The aim of this study was to develop a prognostic model for predicting the outcome of a phase of conservative treatment primarily with physiotherapy in adults with symptomatic PTTs. A prospective observational cohort study was conducted in an outpatient setting in Germany. Ten baseline factors were selected to evaluate nine pre-defined multivariable candidate prognostic models (each including between two and nine factors) in a cohort of adults with symptomatic atraumatic PTTs undergoing a three-month phase of conservative treatment primarily with physiotherapy. The primary outcome was change in the Western Ontario Rotator Cuff Index. The models were developed using linear regression and an information-theoretic analysis approach: Akaike’s Information Criterion (AICC). Eight candidate models were analyzed using data from 61 participants. Two “best models” were identified: smoking & pain catastrophizing and disability & pain catastrophizing. However, none of the models had a satisfactory performance or precision. We could not determine a prognostic model with satisfactory performance and precision. Further high-quality prognostic model studies with larger samples are needed, but should be underpinned, and thus preceded, by robust research that enhances knowledge of relevant prognostic factors. DRKS00004462 . Registered 08 April 2014; retrospectively registered (prior to the analysis).
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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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".