Binary Tönnis classification: simplified modification demonstrates better inter- and intra-observer reliability as well as agreement in surgical management of hip pathology
Bibliographic record
Abstract
BACKGROUND: The traditional Tönnis Classification System has inherent drawbacks as it is vulnerable to the subjectivity of a four-grade system. A two-grade classification could potentially be more reliable. The purpose of this study is to (1) compare the inter-observer and intra-observer reliability of the traditional Tönnis Classification System and a simplified Binary Tönnis Classification System for hip osteoarthritis and to (2) evaluate the clinical applicability of both systems. Our hypothesis is that the proposed Binary Tönnis Classification System will have better reliability and agreement for surgical decision-making. METHODS: Forty consecutive patients were selected to participate in this study. Patients were included in this study if they were between 35 and 60 years old. Patients were excluded if they had prior hip surgeries or conditions. All radiographs were randomized and blinded by a non-observer. Five fellowship-trained hip surgeons from a single center, in a fully crossed design, analyzed and graded all the radiographs utilizing the traditional Tönnis Classification System and the proposed Binary Tönnis Classification System. Intra- and inter-observer reliability values for both the systems were calculated using the Cohen's κ coefficient. A multi-rater κ was calculated using the weighted Fleiss method. RESULTS: The study sample contained 40 anterosuperior hip radiographs. For the traditional Tönnis Classification System, the weighted κ showed a fair inter-observer reliability (κ = 0.474) and excellent intra-observer reliability (κ mean = 0.866). For the proposed Binary Tönnis Classification System, both inter-observer and intra-observer reliability demonstrated excellent values, (κ = 0.858 and 0.928, respectively). On average, the Binary Tönnis Classification System correctly captured 87% of cases. When the traditional Tönnis Classification System was dichotomized, the capture rate was 84%. CONCLUSION: A simplified binary Tönnis Classification System demonstrates better reliability and clinical implementation than the traditional Tönnis Classification System.
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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".