Use of the HOAC II in the management of a complex patient with osteoporosis and multiple traumatic fractures
Bibliographic record
Abstract
This study uses the Hypothesis-Oriented Algorithm for Clinicians II (HOAC II) to describe the physical therapy (PT) management of a patient with osteoporosis is and multiple delayed healing traumatic fractures. Case Description: A 59-year-old female presented with multiple pain areas after a motor vehicle accident that resulted in vertebral, pelvic, rib, and radial fractures. At initial presentation, the fractures had not fully healed, and the patient had a diagnosis of post-concussion syndrome with some cognitive deficits as measured by the Montreal Cognitive Assessment. The patient was limited in functional ability according to the Functional Gait Assessment and the Modified Oswestry Lower Back Pain Questionnaire (OSW). Interventions: The patient received PT intervention as reasoned via the HOAC II method in order to determine the best approach given the hypothesized cause of the patient's impairments. Outcomes: Some improvements in strength and subjective reports of improved mobility were noted, however, the patient showed no clinically important change in level of disability according to the OSW. Discussion: The HOAC 11 model is a useful means of approaching the clinical decision making process of a complex patient because it provides a step-by-step guide for handling multiple patient problems and accounting for changes in patient presentation throughout an episode of care. In a patient case with less than optimal outcomes, the model is a framework in which the student therapist can reason through the weaknesses in the approach and propose specific changes that could improve the patient's quality of life.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".