International Otology Outcome Group and the International Consensus on the Categorization of Tympanomastoid Surgery
Bibliographic record
Abstract
The International Otology Outcome Group (IOOG) was founded in 2017 to encourage and facilitate international collaboration with regard to the surgical outcome of ear surgery. This report outlines the methodology and recommendations of the consensus-based categorization of tympanomastoid surgery produced by the IOOG. The IOOG Steering Committee used the acronym SAMEO-ATO to categorize tympanomastoid operations, representing the stage of surgery, approach, mastoid bone extirpation, external bony wall repair, obliteration of the mastoid cavity, access to the middle ear, tympanic membrane reconstruction, and ossicular reconstruction. A modified Delphi technique was used to obtain international consensus. The expert panels included the chairpersons from 21 otology societies. The approval rate of the SAMEO-ATO system from the otology societies was 95%. The SAMEO-ATO scheme was presented at the 31st Politizer Meeting for field testing. There were no objections or serious concerns raised. Some international otologists wished to see more surgical categories included to reflect the varieties of surgical techniques, but they accepted that it would make the whole system cumbersome. In addition to providing an international categorization of tympanomastoid surgery, the IOOG Steering Committee plans to introduce a common otology dataset that the international otology community could use to record their surgical outcome. The high level of international consensus on the IOOG categorization of tympanomastoid surgery supports this tool for surgeons to pool their surgical data into a large database for research and comparative audit.
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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 | 0.139 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.019 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".