OGSA-PROMs-2023, a collection of musculoskeletal disorders' public open datasets of patient-reported outcome measures (PROMs)
Bibliographic record
Abstract
The datasets in this archive are three patient-reported outcome measure (PROM) musculoskeletal disorders' registry datasets, derived from the knee, hip and spine pathology registries developed and managed since 2018 at the IRCCS Ospedale Galeazzi--Sant'Ambrogio (OGSA) hospital, located in Milan (Italy, EU), one of the major orthopedics research hospitals within the European Union. The three datasets describe around 15 thousand surgical events, corresponding to more than 13 thousand unique patients whose health and quality of life has been monitored across a period of more than years, starting from the preoperatory stage, through a variety of general and pathology-specific PROM' questionnaires. Each record in the datasets represent a surgical event, and a single patient can be associated with multiple surgical events: the complete surgical history of a patient can be reconstructed by matching on the uid column. The datasets contain three main different types of data, collected at different times during the patient surgical and rehabilitation journey and through different acquisition modalities: Clinical and demographical data; Surgical event data; PROM data, including both individual PROM questionnaire items as well as calculated PROM scores. PROM data are collected at pre-operatory time as well as at different follow-ups, namely 3, 6, 12, 24, 60 months after surgery. The data have been pseudo-anonymized by removing potentially identifying fields, including all text fields (except for the operator name, which has been hashed using SHAKE-128).
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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.013 |
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".