Bibliographic record
Abstract
In a series of articles in this issue, Allen proposes and tests a model of movement1; reports the validity and reliability of a self-report instrument, the Movement Ability Measure (MAM)2; and tests the responsiveness to change of the MAM on a small sample of patients.3 The multidimensional model of movement—which includes exibility, strength, accuracy, speed, adaptability, and endurance—specifies the term “movement” at the human, not cellular or molecular, level for the Movement Continuum Theory.4 As noted in Allen's discussion and in the invited commentaries by Cott and Finch, Martin, and Sullivan, the proposed model of movement has limitations, and the assessment tool has not been tested sufficiently to indicate that it is superior to other instruments such as the Outpatient Physical Therapy Improvement in Movement Assessment Log (OPTIMAL)5 or the Activity Measure for Post-Acute Care (AM-PAC) “item bank” and computerized adaptive testing (CAT) assessment platform (AM-PAC-CAT).6 It is clear that we are in the initial rather than final stages of consensus about an instrument that measures outcomes affected by physical therapy intervention and that crosses medical diagnoses, systems, practice settings, acuity, and other variables. Perhaps it is time to sit in a room (real or virtual) to examine these tools and discuss the research that has to be conducted to move us forward instead of sideways. Critical to the discussion: How do these outcome tools mesh with the International Classification of Functioning, Disability and Health (ICD)7?
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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.358 | 0.279 |
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".