Toward Automated Data Extraction According to Tabular Data Structure: Cross-sectional Pilot Survey of the Comparative Clinical Literature
Notice bibliographique
Résumé
BACKGROUND: Systematic reviews depend on time-consuming extraction of data from the PDFs of underlying studies. To date, automation efforts have focused on extracting data from the text, and no approach has yet succeeded in fully automating ingestion of quantitative evidence. However, the majority of relevant data is generally presented in tables, and the tabular structure is more amenable to automated extraction than free text. OBJECTIVE: The purpose of this study was to classify the structure and format of descriptive statistics reported in tables in the comparative medical literature. METHODS: We sampled 100 published randomized controlled trials from 2019 based on a search in PubMed; these results were imported to the AutoLit platform. Studies were excluded if they were nonclinical, noncomparative, not in English, protocols, or not available in full text. In AutoLit, tables reporting baseline or outcome data in all studies were characterized based on reporting practices. Measurement context, meaning the structure in which the interventions of interest, patient arm breakdown, measurement time points, and data element descriptions were presented, was classified based on the number of contextual pieces and metadata reported. The statistic formats for reported metrics (specific instances of reporting of data elements) were then classified by location and broken down into reporting strategies for continuous, dichotomous, and categorical metrics. RESULTS: We included 78 of 100 sampled studies, one of which (1.3%) did not report data elements in tables. The remaining 77 studies reported baseline and outcome data in 174 tables, and 96% (69/72) of these tables broke down reporting by patient arms. Fifteen structures were found for the reporting of measurement context, which were broadly grouped into: 1×1 contexts, where two pieces of context are reported in total (eg, arms in columns, data elements in rows); 2×1 contexts, where two pieces of context are given on row headers (eg, time points in columns, arms nested in data elements on rows); and 1×2 contexts, where two pieces of context are given on column headers. The 1×1 contexts were present in 57% of tables (99/174), compared to 20% (34/174) for 2×1 contexts and 15% (26/174) for 1×2 contexts; the remaining 8% (15/174) used unique/other stratification methods. Statistic formats were reported in the headers or descriptions of 84% (65/74) of studies. CONCLUSIONS: In this cross-sectional pilot review, we found a high density of information in tables, but with major heterogeneity in presentation of measurement context. The highest-density studies reported both baseline and outcome measures in tables, with arm-level breakout, intervention labels, and arm sizes present, and reported both the statistic formats and units. The measurement context formats presented here, broadly classified into three classes that cover 92% (71/78) of studies, form a basis for understanding the frequency of different reporting styles, supporting automated detection of the data format for extraction of metrics.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,499 | 0,757 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,004 | 0,006 |
| Bibliométrie | 0,043 | 0,039 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,009 | 0,013 |
| Science ouverte | 0,003 | 0,009 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».